#load("vcomball20210902.Rda")
load(path(here::here("InitalDataCleaning/Data/vcomball20210902.Rda")))
d <- vcomball
# load("vsurvall20210902.Rda")
# d <- vsurvall

#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes(based on Surveyid)
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
  levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
  levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
  levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
  levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
  levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"

  siteid_new<- siteid
  d<-data.frame(d, siteid_new)
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## factor(0)
## 334 Levels: 800003  800006  800012  800027  800039  800041  800049  800058  800061  800076  800102  800105  ... 801996
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  temp.d <- data.frame (new.d, locationname)

  result<-questionr::freq(temp.d$locationname, total = TRUE)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
n % val%
Los Angeles 334 100 100
Total 334 100 100

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## factor(0)
## 334 Levels: 80100010 80100014 80100017 80100022 80100028 80100029 80100031 80100041 80100043 80100046 ... 80101430
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 0

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete
st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 334 100 100
Online 0 0 0
Total 334 100 100

A1: Date of diagnosis

  • A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
0 1 0.3 0.4
1 29 8.7 11.7
10 28 8.4 11.3
11 13 3.9 5.3
12 14 4.2 5.7
2 13 3.9 5.3
3 23 6.9 9.3
4 24 7.2 9.7
5 28 8.4 11.3
6 22 6.6 8.9
7 17 5.1 6.9
8 19 5.7 7.7
9 16 4.8 6.5
NA 87 26.0 NA
Total 334 100.0 100.0 
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
1965 1 0.3 0.3
2006 1 0.3 0.3
2008 2 0.6 0.6
2009 1 0.3 0.3
2010 1 0.3 0.3
2013 5 1.5 1.6
2014 9 2.7 2.8
2015 78 23.4 24.5
2016 111 33.2 34.8
2017 83 24.9 26.0
2018 25 7.5 7.8
2019 1 0.3 0.3
2020 1 0.3 0.3
NA 15 4.5 NA
Total 334 100.0 100.0 
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 1 0.3 0.3
HAVE_had_ProstateCancer 333 99.7 99.7
Total 334 100.0 100.0

A2: Identify as AA

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
                   Yes="2")
  a2 <- ordered(a2, c("Yes","No"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 311 93.1 100
No 0 0.0 0
NA 23 6.9 NA
Total 334 100.0 100

A3: Black or African American group

  • A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
    • A3_1: 1=Black/African American
    • A3_2: 1=Nigerian
    • A3_3: 1=Jamaican
    • A3_4: 1=Ethiopian
    • A3_5: 1=Haitian
    • A3_6: 1=Somali
    • a3_7: 1=Guyanese
    • A3_8: 1=Creole
    • A3_9: 1=West Indian
    • A3_10: 1=Caribbean
    • A3_11: 1=White
    • A3_12: 1=Asian/Asian American
    • A3_13: 1=Native American or American Indian or Alaskan Native
    • A3_14: 1=Middle Eastern or North African
    • A3_15: 1=Native Hawaiian or Pacific Islander
    • A3_16: 1=Hispanic
    • A3_17: 1=Latino
    • A3_18: 1=Spanish
    • A3_19: 1=Mexican/Mexican American
    • A3_20: 1=Salvadoran
    • A3_21: 1=Puerto Rican
    • A3_22: 1=Dominican
    • A3_23: 1=Columbian
    • A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
  levels(a3_1) <- list(Black_African_American="1")
  new.d <- data.frame(new.d, a3_1)
  new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
  temp.d <- data.frame (new.d, a3_1)
  result<-questionr::freq(temp.d$a3_1, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
n % val%
Black_African_American 310 92.8 100
NA 24 7.2 NA
Total 334 100.0 100 
a3_2 <- as.factor(d[,"a3_2"])
  levels(a3_2) <- list(Nigerian="1")
  new.d <- data.frame(new.d, a3_2)
  new.d <- apply_labels(new.d, a3_2 = "Nigerian")
  temp.d <- data.frame (new.d, a3_2)
  result<-questionr::freq(temp.d$a3_2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
n % val%
Nigerian 8 2.4 100
NA 326 97.6 NA
Total 334 100.0 100 
a3_3 <- as.factor(d[,"a3_3"])
  levels(a3_3) <- list(Jamaican="1")
  new.d <- data.frame(new.d, a3_3)
  new.d <- apply_labels(new.d, a3_3 = "Jamaican")
  temp.d <- data.frame (new.d, a3_3)
  result<-questionr::freq(temp.d$a3_3, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
n % val%
Jamaican 6 1.8 100
NA 328 98.2 NA
Total 334 100.0 100 
a3_4 <- as.factor(d[,"a3_4"])
  levels(a3_4) <- list(Ethiopian="1")
  new.d <- data.frame(new.d, a3_4)
  new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
  temp.d <- data.frame (new.d, a3_4)
  result<-questionr::freq(temp.d$a3_4, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
n % val%
Ethiopian 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_5 <- as.factor(d[,"a3_5"])
  levels(a3_5) <- list(Haitian="1")
  new.d <- data.frame(new.d, a3_5)
  new.d <- apply_labels(new.d, a3_5 = "Haitian")
  temp.d <- data.frame (new.d, a3_5)
  result<-questionr::freq(temp.d$a3_5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
n % val%
Haitian 2 0.6 100
NA 332 99.4 NA
Total 334 100.0 100 
a3_6 <- as.factor(d[,"a3_6"])
  levels(a3_6) <- list(Somali="1")
  new.d <- data.frame(new.d, a3_6)
  new.d <- apply_labels(new.d, a3_6 = "Somali")
  temp.d <- data.frame (new.d, a3_6)
  result<-questionr::freq(temp.d$a3_6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
n % val%
Somali 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_7 <- as.factor(d[,"a3_7"])
  levels(a3_7) <- list(Guyanese="1")
  new.d <- data.frame(new.d, a3_7)
  new.d <- apply_labels(new.d, a3_7 = "Guyanese")
  temp.d <- data.frame (new.d, a3_7)
  result<-questionr::freq(temp.d$a3_7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
n % val%
Guyanese 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_8 <- as.factor(d[,"a3_8"])
  levels(a3_8) <- list(Creole="1")
  new.d <- data.frame(new.d, a3_8)
  new.d <- apply_labels(new.d, a3_8 = "Creole")
  temp.d <- data.frame (new.d, a3_8)
  result<-questionr::freq(temp.d$a3_8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
n % val%
Creole 5 1.5 100
NA 329 98.5 NA
Total 334 100.0 100 
a3_9 <- as.factor(d[,"a3_9"])
  levels(a3_9) <- list(West_Indian="1")
  new.d <- data.frame(new.d, a3_9)
  new.d <- apply_labels(new.d, a3_9 = "West_Indian")
  temp.d <- data.frame (new.d, a3_9)
  result<-questionr::freq(temp.d$a3_9, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
n % val%
West_Indian 3 0.9 100
NA 331 99.1 NA
Total 334 100.0 100 
a3_10 <- as.factor(d[,"a3_10"])
  levels(a3_10) <- list(Caribbean="1")
  new.d <- data.frame(new.d, a3_10)
  new.d <- apply_labels(new.d, a3_10 = "Caribbean")
  temp.d <- data.frame (new.d, a3_10)
  result<-questionr::freq(temp.d$a3_10, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
n % val%
Caribbean 7 2.1 100
NA 327 97.9 NA
Total 334 100.0 100 
a3_11 <- as.factor(d[,"a3_11"])
  levels(a3_11) <- list(White="1")
  new.d <- data.frame(new.d, a3_11)
  new.d <- apply_labels(new.d, a3_11 = "White")
  temp.d <- data.frame (new.d, a3_11)
  result<-questionr::freq(temp.d$a3_11, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
n % val%
White 4 1.2 100
NA 330 98.8 NA
Total 334 100.0 100 
a3_12 <- as.factor(d[,"a3_12"])
  levels(a3_12) <- list(Asian="1")
  new.d <- data.frame(new.d, a3_12)
  new.d <- apply_labels(new.d, a3_12 = "Asian")
  temp.d <- data.frame (new.d, a3_12)
  result<-questionr::freq(temp.d$a3_12, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
n % val%
Asian 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_13 <- as.factor(d[,"a3_13"])
  levels(a3_13) <- list(Native_Indian="1")
  new.d <- data.frame(new.d, a3_13)
  new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
  temp.d <- data.frame (new.d, a3_13)
  result<-questionr::freq(temp.d$a3_13, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
n % val%
Native_Indian 6 1.8 100
NA 328 98.2 NA
Total 334 100.0 100 
a3_14 <- as.factor(d[,"a3_14"])
  levels(a3_14) <- list(Middle_Eastern_North_African="1")
  new.d <- data.frame(new.d, a3_14)
  new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
  temp.d <- data.frame (new.d, a3_14)
  result<-questionr::freq(temp.d$a3_14, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
n % val%
Middle_Eastern_North_African 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_15 <- as.factor(d[,"a3_15"])
  levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
  new.d <- data.frame(new.d, a3_15)
  new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
  temp.d <- data.frame (new.d, a3_15)
  result<-questionr::freq(temp.d$a3_15, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
n % val%
Native_Hawaiian_Pacific_Islander 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
a3_16 <- as.factor(d[,"a3_16"])
  levels(a3_16) <- list(Hispanic="1")
  new.d <- data.frame(new.d, a3_16)
  new.d <- apply_labels(new.d, a3_16 = "Hispanic")
  temp.d <- data.frame (new.d, a3_16)
  result<-questionr::freq(temp.d$a3_16, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
n % val%
Hispanic 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_17 <- as.factor(d[,"a3_17"])
  levels(a3_17) <- list(Latino="1")
  new.d <- data.frame(new.d, a3_17)
  new.d <- apply_labels(new.d, a3_17 = "Latino")
  temp.d <- data.frame (new.d, a3_17)
  result<-questionr::freq(temp.d$a3_17, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
n % val%
Latino 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
a3_18 <- as.factor(d[,"a3_18"])
  levels(a3_18) <- list(Spanish="1")
  new.d <- data.frame(new.d, a3_18)
  new.d <- apply_labels(new.d, a3_18 = "Spanish")
  temp.d <- data.frame (new.d, a3_18)
  result<-questionr::freq(temp.d$a3_18, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
n % val%
Spanish 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_19 <- as.factor(d[,"a3_19"])
  levels(a3_19) <- list(Mexican="1")
  new.d <- data.frame(new.d, a3_19)
  new.d <- apply_labels(new.d, a3_19 = "Mexican")
  temp.d <- data.frame (new.d, a3_19)
  result<-questionr::freq(temp.d$a3_19, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
n % val%
Mexican 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_20 <- as.factor(d[,"a3_20"])
  levels(a3_20) <- list(Salvadoran="1")
  new.d <- data.frame(new.d, a3_20)
  new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
  temp.d <- data.frame (new.d, a3_20)
  result<-questionr::freq(temp.d$a3_20, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
n % val%
Salvadoran 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_21 <- as.factor(d[,"a3_21"])
  levels(a3_21) <- list(Puerto_Rican="1")
  new.d <- data.frame(new.d, a3_21)
  new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
  temp.d <- data.frame (new.d, a3_21)
  result<-questionr::freq(temp.d$a3_21, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
n % val%
Puerto_Rican 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
a3_22 <- as.factor(d[,"a3_22"])
  levels(a3_22) <- list(Dominican="1")
  new.d <- data.frame(new.d, a3_22)
  new.d <- apply_labels(new.d, a3_22 = "Dominican")
  temp.d <- data.frame (new.d, a3_22)
  result<-questionr::freq(temp.d$a3_22, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
n % val%
Dominican 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_23 <- as.factor(d[,"a3_23"])
  levels(a3_23) <- list(Columbian="1")
  new.d <- data.frame(new.d, a3_23)
  new.d <- apply_labels(new.d, a3_23 = "Columbian")
  temp.d <- data.frame (new.d, a3_23)
  result<-questionr::freq(temp.d$a3_23, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
n % val%
Columbian 0 0 NaN
NA 334 100 NA
Total 334 100 100 
a3_24 <- as.factor(d[,"a3_24"])
  levels(a3_23) <- list(Other="1")
  new.d <- data.frame(new.d, a3_24)
  new.d <- apply_labels(new.d, a3_24 = "Other")
  temp.d <- data.frame (new.d, a3_24)
  result<-questionr::freq(temp.d$a3_24, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
n % val%
1 5 1.5 100
NA 329 98.5 NA
Total 334 100.0 100

A3 Other: Black or African American group

a3other <- d[,"a3other"]
  new.d <- data.frame(new.d, a3other)
  new.d <- apply_labels(new.d, a3other = "A3Other")
  temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
n % val%
Belize 1 0.3 16.7
Black British 1 0.3 16.7
Cameroon, Congo (from ethnicity estimate) from DNA sample 1 0.3 16.7
Hebrew Israelite Black 1 0.3 16.7
Negro 1 0.3 16.7
Sierre Leone 1 0.3 16.7
NA 328 98.2 NA
Total 334 100.0 100.0

A4: Month and year of birth

A4. What is your month and year of birth?

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 26 7.8 7.8
10 26 7.8 7.8
11 26 7.8 7.8
12 31 9.3 9.3
2 28 8.4 8.4
24 1 0.3 0.3
3 32 9.6 9.6
4 21 6.3 6.3
5 25 7.5 7.5
58 1 0.3 0.3
6 28 8.4 8.4
7 25 7.5 7.5
8 38 11.4 11.4
9 24 7.2 7.2
NA 2 0.6 NA
Total 334 100.0 100.0 
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1936 1 0.3 0.3
1937 4 1.2 1.2
1938 3 0.9 0.9
1939 3 0.9 0.9
1941 1 0.3 0.3
1942 8 2.4 2.4
1943 10 3.0 3.0
1944 7 2.1 2.1
1945 9 2.7 2.7
1946 15 4.5 4.5
1947 14 4.2 4.2
1948 19 5.7 5.7
1949 17 5.1 5.1
1950 23 6.9 6.9
1951 16 4.8 4.8
1952 21 6.3 6.3
1953 12 3.6 3.6
1954 18 5.4 5.4
1955 17 5.1 5.1
1956 18 5.4 5.4
1957 18 5.4 5.4
1958 12 3.6 3.6
1959 11 3.3 3.3
1960 9 2.7 2.7
1961 10 3.0 3.0
1962 4 1.2 1.2
1963 5 1.5 1.5
1964 3 0.9 0.9
1965 3 0.9 0.9
1966 9 2.7 2.7
1967 5 1.5 1.5
1968 2 0.6 0.6
1969 2 0.6 0.6
1973 2 0.6 0.6
2015 1 0.3 0.3
2018 1 0.3 0.3
NA 1 0.3 NA
Total 334 100.0 100.0

A5: Where were you born

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 310 92.8 94.2
Africa 7 2.1 2.1
Cuba_Caribbean 8 2.4 2.4
Other 4 1.2 1.2
NA 5 1.5 NA
Total 334 100.0 100.0

A5 Other: Where were you born

a5other <- d[,"a5other"]
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "a5other")
  temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
n % val%
Belize, Central America 1 0.3 11.1
Buffalo NY 1 0.3 11.1
Haiti 1 0.3 11.1
Mississippi 1 0.3 11.1
Panama 1 0.3 11.1
Panama City of Panama 1 0.3 11.1
Sierre Leone 1 0.3 11.1
Trinidad. 1 0.3 11.1
UK 1 0.3 11.1
NA 325 97.3 NA
Total 334 100.0 100.0

A6: Biological father born

  • A6. Where was your biological father born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 309 92.5 93.6
Africa 7 2.1 2.1
Cuba_Caribbean 9 2.7 2.7
Other 5 1.5 1.5
NA 4 1.2 NA
Total 334 100.0 100.0

A6 Other: Biological father born

a6other <- d[,"a6other"]
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "a6other")
  temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
n % val%
Belize, Central America 1 0.3 10
Cleveland Mississippi 1 0.3 10
Haiti 1 0.3 10
Jamaica 2 0.6 20
Marshall Texas 1 0.3 10
Panama City of Panama 1 0.3 10
Sierre Leone 1 0.3 10
Trinidad. 1 0.3 10
Unknown 1 0.3 10
NA 324 97.0 NA
Total 334 100.0 100

A7: Biological mother born

  • A7. Where was your biological mother born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 307 91.9 93.3
Africa 7 2.1 2.1
Cuba_Caribbean 10 3.0 3.0
Other 5 1.5 1.5
NA 5 1.5 NA
Total 334 100.0 100.0

A7 Other: Biological father born

a7other <- d[,"a7other"]
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "a7other")
  temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
n % val%
Belize, Central America 1 0.3 10
Buffalo NY 1 0.3 10
Haiti 1 0.3 10
Jamaica 1 0.3 10
Little Rock Arkansas 1 0.3 10
Mexico 1 0.3 10
Panama 1 0.3 10
Panama City of Panama 1 0.3 10
Sierre Leone 1 0.3 10
Trinidad. 1 0.3 10
NA 324 97.0 NA
Total 334 100.0 100

A8: Years lived in the US

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 2 0.6 0.6
years_16_25 2 0.6 0.6
more_than_25_or_whole_life 328 98.2 98.8
NA 2 0.6 NA
Total 334 100.0 100.0

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 205 61.4 62.5
Yes 51 15.3 15.5
Dont_know 72 21.6 22.0
NA 6 1.8 NA
Total 334 100.0 100.0 
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 56 16.8 55.4
Yes 6 1.8 5.9
Dont_know 39 11.7 38.6
NA 233 69.8 NA
Total 334 100.0 100.0 
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  # Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 63 18.9 63
Yes 17 5.1 17
Dont_know 20 6.0 20
NA 234 70.1 NA
Total 334 100.0 100

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 37 11.1 100
NA 297 88.9 NA
Total 334 100.0 100 
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 203 60.8 67.0
Yes 71 21.3 23.4
Dont_know 29 8.7 9.6
NA 31 9.3 NA
Total 334 100.0 100.0 
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 27 8.1 65.9
Two_or_more 14 4.2 34.1
NA 293 87.7 NA
Total 334 100.0 100.0 
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 62 18.6 62.6
Yes 18 5.4 18.2
Dont_know 19 5.7 19.2
NA 235 70.4 NA
Total 334 100.0 100.0 
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  # Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 83 24.9 83
Yes 6 1.8 6
Dont_know 11 3.3 11
NA 234 70.1 NA
Total 334 100.0 100

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 54 16.2 100
NA 280 83.8 NA
Total 334 100.0 100 
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  # Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 265 79.3 95.3
Yes 3 0.9 1.1
Dont_know 10 3.0 3.6
NA 56 16.8 NA
Total 334 100.0 100.0 
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  # Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 4 1.2 50
Two_or_more 4 1.2 50
NA 326 97.6 NA
Total 334 100.0 100 
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  # Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 37 11.1 78.7
Yes 2 0.6 4.3
Dont_know 8 2.4 17.0
NA 287 85.9 NA
Total 334 100.0 100.0 
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  # Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 36 10.8 83.7
Yes 0 0.0 0.0
Dont_know 7 2.1 16.3
NA 291 87.1 NA
Total 334 100.0 100.0

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1da <- ordered(b1da, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 157 47.0 48.6
Yes 7 2.1 2.2
Dont_know 159 47.6 49.2
NA 11 3.3 NA
Total 334 100.0 100.0 
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  # Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1db <- ordered(b1db, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 19 5.7 30.2
Yes 1 0.3 1.6
Dont_know 43 12.9 68.3
NA 271 81.1 NA
Total 334 100.0 100.0 
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  # Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 18 5.4 27.7
Yes 4 1.2 6.2
Dont_know 43 12.9 66.2
NA 269 80.5 NA
Total 334 100.0 100.0

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 142 42.5 44.5
Yes 9 2.7 2.8
Dont_know 168 50.3 52.7
NA 15 4.5 NA
Total 334 100.0 100.0 
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  # Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 20 6.0 31.7
Yes 0 0.0 0.0
Dont_know 43 12.9 68.3
NA 271 81.1 NA
Total 334 100.0 100.0 
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  # Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 24 7.2 36.9
Yes 1 0.3 1.5
Dont_know 40 12.0 61.5
NA 269 80.5 NA
Total 334 100.0 100.0

B2: Family History (Other cancers)

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
    • 2=Yes
    • 1=No
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
                   Yes="2")
  b2 <- ordered(b2, c("Yes","No"))
  
  new.d <- data.frame(new.d, b2)
  new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, b2) 
  
  result<-questionr::freq(temp.d$b2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B2")
B2
n % val%
Yes 100 29.9 41.8
No 139 41.6 58.2
NA 95 28.4 NA
Total 334 100.0 100.0

B2A: Mother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2A_1: 1=Breast
    • B2A_2: 1=Ovarian
    • B2A_3: 1=Colorectal
    • B2A_4: 1=Lung
    • B2A_5: 1=Other Cancer
  b2a_1 <- as.factor(d[,"b2a_1"])
  levels(b2a_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2a_1)
  new.d <- apply_labels(new.d, b2a_1 = "Breast")
  temp.d <- data.frame (new.d, b2a_1)  
  result<-questionr::freq(temp.d$b2a_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 37 11.1 100
NA 297 88.9 NA
Total 334 100.0 100 
  b2a_2 <- as.factor(d[,"b2a_2"])
  levels(b2a_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2a_2)
  new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2a_2)  
  result<-questionr::freq(temp.d$b2a_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 10 3 100
NA 324 97 NA
Total 334 100 100 
  b2a_3 <- as.factor(d[,"b2a_3"])
  levels(b2a_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2a_3)
  new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2a_3)  
  
  result<-questionr::freq(temp.d$b2a_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 6 1.8 100
NA 328 98.2 NA
Total 334 100.0 100 
  b2a_4 <- as.factor(d[,"b2a_4"])
  levels(b2a_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2a_4)
  new.d <- apply_labels(new.d, b2a_4 = "Lung")
  temp.d <- data.frame (new.d, b2a_4)  
  
  result<-questionr::freq(temp.d$b2a_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 10 3 100
NA 324 97 NA
Total 334 100 100 
  b2a_5 <- as.factor(d[,"b2a_5"])
  levels(b2a_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2a_5)
  new.d <- apply_labels(new.d, b2a_5 = "Lung")
  temp.d <- data.frame (new.d, b2a_5)  
  
  result<-questionr::freq(temp.d$b2a_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 31 9.3 100
NA 303 90.7 NA
Total 334 100.0 100

B2B: Father

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2B_1: 1=Breast
    • B2B_3: 1=Colorectal
    • B2B_4: 1=Lung
    • B2B_5: 1=Other Cancer
  b2b_1 <- as.factor(d[,"b2b_1"])
  levels(b2b_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2b_1)
  new.d <- apply_labels(new.d, b2b_1 = "Breast")
  temp.d <- data.frame (new.d, b2b_1)  
  result<-questionr::freq(temp.d$b2b_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
  b2b_3 <- as.factor(d[,"b2b_3"])
  levels(b2b_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2b_3)
  new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2b_3)  
  
  result<-questionr::freq(temp.d$b2b_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 9 2.7 100
NA 325 97.3 NA
Total 334 100.0 100 
  b2b_4 <- as.factor(d[,"b2b_4"])
  levels(b2b_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2b_4)
  new.d <- apply_labels(new.d, b2b_4 = "Lung")
  temp.d <- data.frame (new.d, b2b_4)  
  
  result<-questionr::freq(temp.d$b2b_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 19 5.7 100
NA 315 94.3 NA
Total 334 100.0 100 
  b2b_5 <- as.factor(d[,"b2b_5"])
  levels(b2b_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2b_5)
  new.d <- apply_labels(new.d, b2b_5 = "Lung")
  temp.d <- data.frame (new.d, b2b_5)  
  
  result<-questionr::freq(temp.d$b2b_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 23 6.9 100
NA 311 93.1 NA
Total 334 100.0 100

B2C: Any sister

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2C_1: 1=Breast
    • B2C_2: 1=Ovarian
    • B2C_3: 1=Colorectal
    • B2C_4: 1=Lung
    • B2C_5: 1=Other Cancer
  b2c_1 <- as.factor(d[,"b2c_1"])
  levels(b2c_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2c_1)
  new.d <- apply_labels(new.d, b2c_1 = "Breast")
  temp.d <- data.frame (new.d, b2c_1)  
  result<-questionr::freq(temp.d$b2c_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 31 9.3 100
NA 303 90.7 NA
Total 334 100.0 100 
  b2c_2 <- as.factor(d[,"b2c_2"])
  levels(b2c_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2c_2)
  new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2c_2)  
  result<-questionr::freq(temp.d$b2c_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 7 2.1 100
NA 327 97.9 NA
Total 334 100.0 100 
  b2c_3 <- as.factor(d[,"b2c_3"])
  levels(b2c_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2c_3)
  new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2c_3)  
  
  result<-questionr::freq(temp.d$b2c_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 5 1.5 100
NA 329 98.5 NA
Total 334 100.0 100 
  b2c_4 <- as.factor(d[,"b2c_4"])
  levels(b2c_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2c_4)
  new.d <- apply_labels(new.d, b2c_4 = "Lung")
  temp.d <- data.frame (new.d, b2c_4)  
  
  result<-questionr::freq(temp.d$b2c_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 8 2.4 100
NA 326 97.6 NA
Total 334 100.0 100 
  b2c_5 <- as.factor(d[,"b2c_5"])
  levels(b2c_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2c_5)
  new.d <- apply_labels(new.d, b2c_5 = "Lung")
  temp.d <- data.frame (new.d, b2c_5)  
  
  result<-questionr::freq(temp.d$b2c_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 19 5.7 100
NA 315 94.3 NA
Total 334 100.0 100

B2D: Any brother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2D_1: 1=Breast
    • B2D_3: 1=Colorectal
    • B2D_4: 1=Lung
    • B2D_5: 1=Other Cancer
  b2d_1 <- as.factor(d[,"b2d_1"])
  levels(b2d_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2d_1)
  new.d <- apply_labels(new.d, b2d_1 = "Breast")
  temp.d <- data.frame (new.d, b2d_1)  
  result<-questionr::freq(temp.d$b2d_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 3 0.9 100
NA 331 99.1 NA
Total 334 100.0 100 
  b2d_3 <- as.factor(d[,"b2d_3"])
  levels(b2d_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2d_3)
  new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2d_3)  
  
  result<-questionr::freq(temp.d$b2d_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 5 1.5 100
NA 329 98.5 NA
Total 334 100.0 100 
  b2d_4 <- as.factor(d[,"b2d_4"])
  levels(b2d_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2d_4)
  new.d <- apply_labels(new.d, b2d_4 = "Lung")
  temp.d <- data.frame (new.d, b2d_4)  
  
  result<-questionr::freq(temp.d$b2d_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 8 2.4 100
NA 326 97.6 NA
Total 334 100.0 100 
  b2d_5 <- as.factor(d[,"b2d_5"])
  levels(b2d_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2d_5)
  new.d <- apply_labels(new.d, b2d_5 = "Lung")
  temp.d <- data.frame (new.d, b2d_5)  
  
  result<-questionr::freq(temp.d$b2d_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 21 6.3 100
NA 313 93.7 NA
Total 334 100.0 100

B2E: Any daughter

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2E_1: 1=Breast
    • B2E_2: 1=Ovarian
    • B2E_3: 1=Colorectal
    • B2E_4: 1=Lung
    • B2E_5: 1=Other Cancer
  b2e_1 <- as.factor(d[,"b2e_1"])
  levels(b2e_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2e_1)
  new.d <- apply_labels(new.d, b2e_1 = "Breast")
  temp.d <- data.frame (new.d, b2e_1)  
  result<-questionr::freq(temp.d$b2e_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 2 0.6 100
NA 332 99.4 NA
Total 334 100.0 100 
  b2e_2 <- as.factor(d[,"b2e_2"])
  levels(b2e_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2e_2)
  new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2e_2)  
  result<-questionr::freq(temp.d$b2e_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 3 0.9 100
NA 331 99.1 NA
Total 334 100.0 100 
  b2e_3 <- as.factor(d[,"b2e_3"])
  levels(b2e_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2e_3)
  new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2e_3)  
  
  result<-questionr::freq(temp.d$b2e_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
  b2e_4 <- as.factor(d[,"b2e_4"])
  levels(b2e_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2e_4)
  new.d <- apply_labels(new.d, b2e_4 = "Lung")
  temp.d <- data.frame (new.d, b2e_4)  
  
  result<-questionr::freq(temp.d$b2e_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 0 0 NaN
NA 334 100 NA
Total 334 100 100 
  b2e_5 <- as.factor(d[,"b2e_5"])
  levels(b2e_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2e_5)
  new.d <- apply_labels(new.d, b2e_5 = "Lung")
  temp.d <- data.frame (new.d, b2e_5)  
  
  result<-questionr::freq(temp.d$b2e_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100

B2F: Any son

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2F_1: 1=Breast
    • B2F_3: 1=Colorectal
    • B2F_4: 1=Lung
    • B2F_5: 1=Other Cancer
  b2f_1 <- as.factor(d[,"b2f_1"])
  levels(b2f_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2f_1)
  new.d <- apply_labels(new.d, b2f_1 = "Breast")
  temp.d <- data.frame (new.d, b2f_1)  
  result<-questionr::freq(temp.d$b2f_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 334 100 NA
Total 334 100 100 
  b2f_3 <- as.factor(d[,"b2f_3"])
  levels(b2f_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2f_3)
  new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2f_3)  
  
  result<-questionr::freq(temp.d$b2f_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 0 0 NaN
NA 334 100 NA
Total 334 100 100 
  b2f_4 <- as.factor(d[,"b2f_4"])
  levels(b2f_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2f_4)
  new.d <- apply_labels(new.d, b2f_4 = "Lung")
  temp.d <- data.frame (new.d, b2f_4)  
  
  result<-questionr::freq(temp.d$b2f_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 0 0 NaN
NA 334 100 NA
Total 334 100 100 
  b2f_5 <- as.factor(d[,"b2f_5"])
  levels(b2f_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2f_5)
  new.d <- apply_labels(new.d, b2f_5 = "Lung")
  temp.d <- data.frame (new.d, b2f_5)  
  
  result<-questionr::freq(temp.d$b2f_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 2 0.6 100
NA 332 99.4 NA
Total 334 100.0 100

B3: Current health

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor
  b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 28 8.4 8.6 8.4 8.6
Very_Good 104 31.1 32.0 39.5 40.6
Good 127 38.0 39.1 77.5 79.7
Fair 55 16.5 16.9 94.0 96.6
Poor 11 3.3 3.4 97.3 100.0
NA 9 2.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

B4: Comorbidities

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer
# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
  levels(b4aa) <- list(No="1",
                     Yes="2")
  b4aa <- ordered(b4aa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 300 89.8 92
Yes 26 7.8 8
NA 8 2.4 NA
Total 334 100.0 100 
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
25 1 0.3 4
38 1 0.3 4
40 2 0.6 8
42 1 0.3 4
48 1 0.3 4
50 3 0.9 12
51 1 0.3 4
52 2 0.6 8
54 2 0.6 8
55 2 0.6 8
58 1 0.3 4
60 2 0.6 8
61 1 0.3 4
63 1 0.3 4
65 1 0.3 4
66 1 0.3 4
69 1 0.3 4
71 1 0.3 4
NA 309 92.5 NA
Total 334 100.0 100 
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  # Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
  levels(b4ba) <- list(No="1",
                     Yes="2")
  b4ba <- ordered(b4ba, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 304 91.0 93.5
Yes 21 6.3 6.5
NA 9 2.7 NA
Total 334 100.0 100.0 
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
12 1 0.3 5.3
13 1 0.3 5.3
25 1 0.3 5.3
40 1 0.3 5.3
42 1 0.3 5.3
45 1 0.3 5.3
55 1 0.3 5.3
56 1 0.3 5.3
60 2 0.6 10.5
62 1 0.3 5.3
63 1 0.3 5.3
68 2 0.6 10.5
70 1 0.3 5.3
71 2 0.6 10.5
80 1 0.3 5.3
83 1 0.3 5.3
NA 315 94.3 NA
Total 334 100.0 100.0 
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  # Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
  levels(b4ca) <- list(No="1",
                     Yes="2")
  b4ca <- ordered(b4ca, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 300 89.8 93.2
Yes 22 6.6 6.8
NA 12 3.6 NA
Total 334 100.0 100.0 
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
45 2 0.6 9.5
46 1 0.3 4.8
47 1 0.3 4.8
50 1 0.3 4.8
52 1 0.3 4.8
57 1 0.3 4.8
58 1 0.3 4.8
60 1 0.3 4.8
61 2 0.6 9.5
62 1 0.3 4.8
63 3 0.9 14.3
66 2 0.6 9.5
67 1 0.3 4.8
68 1 0.3 4.8
69 2 0.6 9.5
NA 313 93.7 NA
Total 334 100.0 100.0 
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
  levels(b4da) <- list(No="1",
                     Yes="2")
  b4da <- ordered(b4da, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 99 29.6 30.7
Yes 224 67.1 69.3
NA 11 3.3 NA
Total 334 100.0 100.0 
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
10 1 0.3 0.5
18 1 0.3 0.5
2 1 0.3 0.5
22 1 0.3 0.5
24 1 0.3 0.5
25 2 0.6 1.0
27 1 0.3 0.5
28 1 0.3 0.5
29 1 0.3 0.5
30 5 1.5 2.4
32 6 1.8 2.9
34 1 0.3 0.5
35 8 2.4 3.8
36 1 0.3 0.5
37 2 0.6 1.0
38 5 1.5 2.4
39 2 0.6 1.0
40 14 4.2 6.7
41 3 0.9 1.4
42 6 1.8 2.9
43 2 0.6 1.0
44 2 0.6 1.0
45 23 6.9 11.1
46 3 0.9 1.4
47 1 0.3 0.5
48 7 2.1 3.4
49 2 0.6 1.0
50 17 5.1 8.2
52 1 0.3 0.5
53 1 0.3 0.5
55 14 4.2 6.7
56 7 2.1 3.4
57 3 0.9 1.4
58 7 2.1 3.4
59 1 0.3 0.5
60 16 4.8 7.7
61 5 1.5 2.4
62 6 1.8 2.9
63 3 0.9 1.4
65 9 2.7 4.3
68 7 2.1 3.4
69 1 0.3 0.5
72 3 0.9 1.4
9 1 0.3 0.5
91 1 0.3 0.5
94 1 0.3 0.5
98 1 0.3 0.5
NA 126 37.7 NA
Total 334 100.0 100.0 
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"  
  levels(b4ea) <- list(No="1",
                     Yes="2")
  b4ea <- ordered(b4ea, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 312 93.4 96.9
Yes 10 3.0 3.1
NA 12 3.6 NA
Total 334 100.0 100.0 
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
40 1 0.3 5.6
42 1 0.3 5.6
48 1 0.3 5.6
51 1 0.3 5.6
54 1 0.3 5.6
55 1 0.3 5.6
58 3 0.9 16.7
60 1 0.3 5.6
62 2 0.6 11.1
64 2 0.6 11.1
65 3 0.9 16.7
70 1 0.3 5.6
NA 316 94.6 NA
Total 334 100.0 100.0 
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  # Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
  levels(b4fa) <- list(No="1",
                     Yes="2")
  b4fa <- ordered(b4fa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
n % val%
No 144 43.1 44.6
Yes 179 53.6 55.4
NA 11 3.3 NA
Total 334 100.0 100.0 
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
10 1 0.3 0.6
18 1 0.3 0.6
32 1 0.3 0.6
34 1 0.3 0.6
35 3 0.9 1.9
36 1 0.3 0.6
38 1 0.3 0.6
39 2 0.6 1.3
40 7 2.1 4.5
41 1 0.3 0.6
42 4 1.2 2.6
43 2 0.6 1.3
44 2 0.6 1.3
45 9 2.7 5.8
48 6 1.8 3.9
5 1 0.3 0.6
50 17 5.1 11.0
51 2 0.6 1.3
52 4 1.2 2.6
55 18 5.4 11.7
56 1 0.3 0.6
57 2 0.6 1.3
58 13 3.9 8.4
59 2 0.6 1.3
60 15 4.5 9.7
61 1 0.3 0.6
62 6 1.8 3.9
63 2 0.6 1.3
64 4 1.2 2.6
65 4 1.2 2.6
67 1 0.3 0.6
68 4 1.2 2.6
69 4 1.2 2.6
70 1 0.3 0.6
71 1 0.3 0.6
72 2 0.6 1.3
73 1 0.3 0.6
74 2 0.6 1.3
75 1 0.3 0.6
9 1 0.3 0.6
91 1 0.3 0.6
94 1 0.3 0.6
NA 180 53.9 NA
Total 334 100.0 100.0 
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  # Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
  levels(b4ga) <- list(No="1",
                     Yes="2")
  b4ga <- ordered(b4ga, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
n % val%
No 281 84.1 84.9
Yes 50 15.0 15.1
NA 3 0.9 NA
Total 334 100.0 100.0 
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
10 3 0.9 6.2
12 1 0.3 2.1
14 1 0.3 2.1
20 1 0.3 2.1
22 1 0.3 2.1
32 1 0.3 2.1
35 1 0.3 2.1
4 3 0.9 6.2
40 1 0.3 2.1
45 1 0.3 2.1
48 1 0.3 2.1
5 4 1.2 8.3
50 1 0.3 2.1
52 1 0.3 2.1
55 1 0.3 2.1
58 1 0.3 2.1
59 1 0.3 2.1
6 4 1.2 8.3
60 4 1.2 8.3
61 2 0.6 4.2
63 2 0.6 4.2
65 1 0.3 2.1
69 1 0.3 2.1
7 3 0.9 6.2
70 2 0.6 4.2
78 1 0.3 2.1
8 3 0.9 6.2
80 1 0.3 2.1
NA 286 85.6 NA
Total 334 100.0 100.0 
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  # Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
  levels(b4ha) <- list(No="1",
                     Yes="2")
  b4ha <- ordered(b4ha, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 307 91.9 93
Yes 23 6.9 7
NA 4 1.2 NA
Total 334 100.0 100 
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
10 1 0.3 5
13 1 0.3 5
15 1 0.3 5
19 1 0.3 5
22 2 0.6 10
23 1 0.3 5
25 1 0.3 5
28 1 0.3 5
3 1 0.3 5
32 1 0.3 5
34 1 0.3 5
35 1 0.3 5
38 1 0.3 5
40 1 0.3 5
44 1 0.3 5
50 2 0.6 10
52 1 0.3 5
59 1 0.3 5
NA 314 94.0 NA
Total 334 100.0 100 
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  # Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
  levels(b4ia) <- list(No="1",
                     Yes="2")
  b4ia <- ordered(b4ia, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 317 94.9 96.6
Yes 11 3.3 3.4
NA 6 1.8 NA
Total 334 100.0 100.0 
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
22 1 0.3 10
32 1 0.3 10
38 1 0.3 10
40 2 0.6 20
50 2 0.6 20
55 3 0.9 30
NA 324 97.0 NA
Total 334 100.0 100 
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  # Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
  levels(b4ja) <- list(No="1",
                     Yes="2")
  b4ja <- ordered(b4ja, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 232 69.5 70.5
Yes 97 29.0 29.5
NA 5 1.5 NA
Total 334 100.0 100.0 
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
17 2 0.6 2.3
3 1 0.3 1.2
30 2 0.6 2.3
35 1 0.3 1.2
38 1 0.3 1.2
40 2 0.6 2.3
42 2 0.6 2.3
44 1 0.3 1.2
45 8 2.4 9.3
48 2 0.6 2.3
50 5 1.5 5.8
51 2 0.6 2.3
52 3 0.9 3.5
53 2 0.6 2.3
54 3 0.9 3.5
55 6 1.8 7.0
56 1 0.3 1.2
57 1 0.3 1.2
58 5 1.5 5.8
59 1 0.3 1.2
60 5 1.5 5.8
61 5 1.5 5.8
62 8 2.4 9.3
63 1 0.3 1.2
64 2 0.6 2.3
65 5 1.5 5.8
66 4 1.2 4.7
69 1 0.3 1.2
71 1 0.3 1.2
75 1 0.3 1.2
76 1 0.3 1.2
94 1 0.3 1.2
NA 248 74.3 NA
Total 334 100.0 100.0 
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  # Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
  levels(b4ka) <- list(No="1",
                     Yes="2")
  b4ka <- ordered(b4ka, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 315 94.3 95.2
Yes 16 4.8 4.8
NA 3 0.9 NA
Total 334 100.0 100.0 
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
35 1 0.3 6.7
45 1 0.3 6.7
52 1 0.3 6.7
55 2 0.6 13.3
60 2 0.6 13.3
64 1 0.3 6.7
65 2 0.6 13.3
68 2 0.6 13.3
70 1 0.3 6.7
72 1 0.3 6.7
80 1 0.3 6.7
NA 319 95.5 NA
Total 334 100.0 100.0 
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  # Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
  levels(b4la) <- list(No="1",
                     Yes="2")
  b4la <- ordered(b4la, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 324 97.0 97.9
Yes 7 2.1 2.1
NA 3 0.9 NA
Total 334 100.0 100.0 
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
45 1 0.3 16.7
50 1 0.3 16.7
58 1 0.3 16.7
60 2 0.6 33.3
69 1 0.3 16.7
NA 328 98.2 NA
Total 334 100.0 100.0 
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  # Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
  levels(b4ma) <- list(No="1",
                     Yes="2")
  b4ma <- ordered(b4ma, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 288 86.2 87.8
Yes 40 12.0 12.2
NA 6 1.8 NA
Total 334 100.0 100.0 
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
1 1 0.3 2.9
10 1 0.3 2.9
20 1 0.3 2.9
21 1 0.3 2.9
28 1 0.3 2.9
30 1 0.3 2.9
35 1 0.3 2.9
36 1 0.3 2.9
40 3 0.9 8.8
41 1 0.3 2.9
45 1 0.3 2.9
48 1 0.3 2.9
50 4 1.2 11.8
51 1 0.3 2.9
53 1 0.3 2.9
54 1 0.3 2.9
57 1 0.3 2.9
60 3 0.9 8.8
61 1 0.3 2.9
62 3 0.9 8.8
63 2 0.6 5.9
64 2 0.6 5.9
76 1 0.3 2.9
NA 300 89.8 NA
Total 334 100.0 100.0 
# Dementia
  b4na <- as.factor(d[,"b4na"])
  # Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
  levels(b4na) <- list(No="1",
                     Yes="2")
  b4na <- ordered(b4na, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 330 98.8 99.4
Yes 2 0.6 0.6
NA 2 0.6 NA
Total 334 100.0 100.0 
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
63 1 0.3 50
72 1 0.3 50
NA 332 99.4 NA
Total 334 100.0 100 
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  # Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
  levels(b4oa) <- list(No="1",
                     Yes="2")
  b4oa <- ordered(b4oa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 289 86.5 87.3
Yes 42 12.6 12.7
NA 3 0.9 NA
Total 334 100.0 100.0 
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
22 1 0.3 3.4
25 2 0.6 6.9
28 1 0.3 3.4
29 1 0.3 3.4
35 2 0.6 6.9
37 1 0.3 3.4
39 1 0.3 3.4
42 1 0.3 3.4
45 1 0.3 3.4
48 1 0.3 3.4
50 1 0.3 3.4
51 1 0.3 3.4
52 1 0.3 3.4
55 3 0.9 10.3
59 1 0.3 3.4
60 1 0.3 3.4
61 2 0.6 6.9
64 2 0.6 6.9
65 3 0.9 10.3
72 1 0.3 3.4
73 1 0.3 3.4
NA 305 91.3 NA
Total 334 100.0 100.0 
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  # Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
  levels(b4pa) <- list(No="1",
                     Yes="2")
  b4pa <- ordered(b4pa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 329 98.5 99.4
Yes 2 0.6 0.6
NA 3 0.9 NA
Total 334 100.0 100.0 
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
65 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  # Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
  levels(b4qa) <- list(No="1",
                     Yes="2")
  b4qa <- ordered(b4qa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 306 91.6 95
Yes 16 4.8 5
NA 12 3.6 NA
Total 334 100.0 100 
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
19 1 0.3 5.9
35 1 0.3 5.9
41 1 0.3 5.9
44 1 0.3 5.9
47 1 0.3 5.9
55 1 0.3 5.9
58 1 0.3 5.9
60 2 0.6 11.8
62 2 0.6 11.8
66 1 0.3 5.9
68 1 0.3 5.9
7 1 0.3 5.9
72 1 0.3 5.9
73 1 0.3 5.9
80 1 0.3 5.9
NA 317 94.9 NA
Total 334 100.0 100.0

B4Q Other Cancer

b4qother <- d[,"b4qother"]
  new.d <- data.frame(new.d, b4qother)
  new.d <- apply_labels(new.d, b4qother = "b4qother")
  temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
n % val%
#NAME? 1 0.3 6.7
Anal 1 0.3 6.7
Bone marrow cancer. 1 0.3 6.7
Kidney 1 0.3 6.7
Kidney (left kidney removed). 1 0.3 6.7
Kidney. 1 0.3 6.7
Leukemia (CML). 1 0.3 6.7
Lung 2 0.6 13.3
Lymphoma/Colon 1 0.3 6.7
Multiple Myeloma 1 0.3 6.7
Non Hodgkins Lymphoma 1 0.3 6.7
Skin 1 0.3 6.7
Skin cancer. 1 0.3 6.7
Spread to liver and pelvic area. 1 0.3 6.7
NA 319 95.5 NA
Total 334 100.0 100.0

B5: Routine care

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location
  b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 19 5.7 6.0
Hospital_urgent_care_clinic 20 6.0 6.3
Private_Dr_office 253 75.7 80.1
ER 4 1.2 1.3
VA 14 4.2 4.4
Other 6 1.8 1.9
NA 18 5.4 NA
Total 334 100.0 100.0

B5 Other: Routine care

b5other <- d[,"b5other"]
  new.d <- data.frame(new.d, b5other)
  new.d <- apply_labels(new.d, b5other = "b5other")
  temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
n % val%
Family practice. 1 0.3 6.7
Kaiser 2 0.6 13.3
Kaiser Hospital 1 0.3 6.7
Kaiser Permanently 1 0.3 6.7
Kaiser. 2 0.6 13.3
LA Care 1 0.3 6.7
Medicaid 1 0.3 6.7
Medicare and medical —- 1 0.3 6.7
MPTF (UCLA) 1 0.3 6.7
Private doctor 1 0.3 6.7
Scan. 1 0.3 6.7
Scan/UCLA. 1 0.3 6.7
UCLA Hospital 1 0.3 6.7
NA 319 95.5 NA
Total 334 100.0 100.0

C1: Years lived at current address

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years
  c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 20 6.0 6.0 6.0 6.0
years_1_5 66 19.8 19.8 25.7 25.8
years_6_10 41 12.3 12.3 38.0 38.1
years_11_15 25 7.5 7.5 45.5 45.6
years_16_20 41 12.3 12.3 57.8 58.0
years_21_more 140 41.9 42.0 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C2A: Feel safe walking in the neighborhood

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  # Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
  levels(c2a2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  # Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
  levels(c2a3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 137 41.0 42.3 41.0 42.3
Agree 114 34.1 35.2 75.1 77.5
Neutral 50 15.0 15.4 90.1 92.9
Disagree 20 6.0 6.2 96.1 99.1
Strongly_Disagree 3 0.9 0.9 97.0 100.0
NA 10 3.0 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 113 33.8 35.4 33.8 35.4
Agree 128 38.3 40.1 72.2 75.5
Neutral 49 14.7 15.4 86.8 90.9
Disagree 24 7.2 7.5 94.0 98.4
Strongly_Disagree 5 1.5 1.6 95.5 100.0
NA 15 4.5 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 136 40.7 42.9 40.7 42.9
Agree 92 27.5 29.0 68.3 71.9
Neutral 55 16.5 17.4 84.7 89.3
Disagree 25 7.5 7.9 92.2 97.2
Strongly_Disagree 9 2.7 2.8 94.9 100.0
NA 17 5.1 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C2B: Violence

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  # Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
  levels(c2b2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  # Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
  levels(c2b3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 102 30.5 31.6 30.5 31.6
Agree 105 31.4 32.5 62.0 64.1
Neutral 64 19.2 19.8 81.1 83.9
Disagree 41 12.3 12.7 93.4 96.6
Strongly_Disagree 11 3.3 3.4 96.7 100.0
NA 11 3.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 78 23.4 24.5 23.4 24.5
Agree 116 34.7 36.4 58.1 60.8
Neutral 83 24.9 26.0 82.9 86.8
Disagree 31 9.3 9.7 92.2 96.6
Strongly_Disagree 11 3.3 3.4 95.5 100.0
NA 15 4.5 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 103 30.8 32.4 30.8 32.4
Agree 97 29.0 30.5 59.9 62.9
Neutral 64 19.2 20.1 79.0 83.0
Disagree 42 12.6 13.2 91.6 96.2
Strongly_Disagree 12 3.6 3.8 95.2 100.0
NA 16 4.8 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C2C: Safe from crime

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  # Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
  levels(c2c2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  # Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
  levels(c2c3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 76 22.8 23.6 22.8 23.6
Agree 93 27.8 28.9 50.6 52.5
Neutral 80 24.0 24.8 74.6 77.3
Disagree 61 18.3 18.9 92.8 96.3
Strongly_Disagree 12 3.6 3.7 96.4 100.0
NA 12 3.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 58 17.4 18.2 17.4 18.2
Agree 106 31.7 33.3 49.1 51.6
Neutral 91 27.2 28.6 76.3 80.2
Disagree 51 15.3 16.0 91.6 96.2
Strongly_Disagree 12 3.6 3.8 95.2 100.0
NA 16 4.8 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 86 25.7 27.2 25.7 27.2
Agree 79 23.7 25.0 49.4 52.2
Neutral 82 24.6 25.9 74.0 78.2
Disagree 55 16.5 17.4 90.4 95.6
Strongly_Disagree 14 4.2 4.4 94.6 100.0
NA 18 5.4 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C3A: Traffic

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  # Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  # Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 175 52.4 53.4 52.4 53.4
Somewhat_serious 96 28.7 29.3 81.1 82.6
Very_serious 51 15.3 15.5 96.4 98.2
Dont_know 6 1.8 1.8 98.2 100.0
NA 6 1.8 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 173 51.8 53.2 51.8 53.2
Somewhat_serious 116 34.7 35.7 86.5 88.9
Very_serious 27 8.1 8.3 94.6 97.2
Dont_know 9 2.7 2.8 97.3 100.0
NA 9 2.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 241 72.2 75.1 72.2 75.1
Somewhat_serious 50 15.0 15.6 87.1 90.7
Very_serious 12 3.6 3.7 90.7 94.4
Dont_know 18 5.4 5.6 96.1 100.0
NA 13 3.9 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C3B: Noise

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  # Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  # Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 239 71.6 73.5 71.6 73.5
Somewhat_serious 64 19.2 19.7 90.7 93.2
Very_serious 17 5.1 5.2 95.8 98.5
Dont_know 5 1.5 1.5 97.3 100.0
NA 9 2.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 220 65.9 68.3 65.9 68.3
Somewhat_serious 79 23.7 24.5 89.5 92.9
Very_serious 13 3.9 4.0 93.4 96.9
Dont_know 10 3.0 3.1 96.4 100.0
NA 12 3.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 239 71.6 74.7 71.6 74.7
Somewhat_serious 52 15.6 16.2 87.1 90.9
Very_serious 11 3.3 3.4 90.4 94.4
Dont_know 18 5.4 5.6 95.8 100.0
NA 14 4.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C3C: Trash and litter

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  # Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  # Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 247 74.0 76.0 74.0 76.0
Somewhat_serious 56 16.8 17.2 90.7 93.2
Very_serious 20 6.0 6.2 96.7 99.4
Dont_know 2 0.6 0.6 97.3 100.0
NA 9 2.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 240 71.9 75.2 71.9 75.2
Somewhat_serious 65 19.5 20.4 91.3 95.6
Very_serious 8 2.4 2.5 93.7 98.1
Dont_know 6 1.8 1.9 95.5 100.0
NA 15 4.5 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 264 79.0 82.5 79.0 82.5
Somewhat_serious 40 12.0 12.5 91.0 95.0
Very_serious 7 2.1 2.2 93.1 97.2
Dont_know 9 2.7 2.8 95.8 100.0
NA 14 4.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C3D: Too much light at night

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  # Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  # Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 299 89.5 92.3 89.5 92.3
Somewhat_serious 15 4.5 4.6 94.0 96.9
Very_serious 4 1.2 1.2 95.2 98.1
Dont_know 6 1.8 1.9 97.0 100.0
NA 10 3.0 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 292 87.4 90.4 87.4 90.4
Somewhat_serious 24 7.2 7.4 94.6 97.8
Very_serious 0 0.0 0.0 94.6 97.8
Dont_know 7 2.1 2.2 96.7 100.0
NA 11 3.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 286 85.6 89.4 85.6 89.4
Somewhat_serious 16 4.8 5.0 90.4 94.4
Very_serious 1 0.3 0.3 90.7 94.7
Dont_know 17 5.1 5.3 95.8 100.0
NA 14 4.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C4A: Neighbors talking outside

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA" 
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  # Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 127 38.0 38.7 38.0 38.7
Sometimes 140 41.9 42.7 79.9 81.4
Rarely_Never 59 17.7 18.0 97.6 99.4
Dont_know 2 0.6 0.6 98.2 100.0
NA 6 1.8 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 124 37.1 38.4 37.1 38.4
Sometimes 149 44.6 46.1 81.7 84.5
Rarely_Never 41 12.3 12.7 94.0 97.2
Dont_know 9 2.7 2.8 96.7 100.0
NA 11 3.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 197 59.0 61.6 59.0 61.6
Sometimes 83 24.9 25.9 83.8 87.5
Rarely_Never 26 7.8 8.1 91.6 95.6
Dont_know 14 4.2 4.4 95.8 100.0
NA 14 4.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C4B: Neighbors watch out for each other

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  # Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  # Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 124 37.1 38.0 37.1 38.0
Sometimes 118 35.3 36.2 72.5 74.2
Rarely_Never 53 15.9 16.3 88.3 90.5
Dont_know 31 9.3 9.5 97.6 100.0
NA 8 2.4 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 113 33.8 35.4 33.8 35.4
Sometimes 138 41.3 43.3 75.1 78.7
Rarely_Never 47 14.1 14.7 89.2 93.4
Dont_know 21 6.3 6.6 95.5 100.0
NA 15 4.5 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 186 55.7 59.0 55.7 59.0
Sometimes 79 23.7 25.1 79.3 84.1
Rarely_Never 22 6.6 7.0 85.9 91.1
Dont_know 28 8.4 8.9 94.3 100.0
NA 19 5.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C4C: Neighbors know by name

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 72 21.6 22.5 21.6 22.5
Sometimes 142 42.5 44.4 64.1 66.9
Rarely_Never 103 30.8 32.2 94.9 99.1
Dont_know 3 0.9 0.9 95.8 100.0
NA 14 4.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 87 26.0 27.7 26.0 27.7
Sometimes 147 44.0 46.8 70.1 74.5
Rarely_Never 77 23.1 24.5 93.1 99.0
Dont_know 3 0.9 1.0 94.0 100.0
NA 20 6.0 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 188 56.3 60.6 56.3 60.6
Sometimes 73 21.9 23.5 78.1 84.2
Rarely_Never 39 11.7 12.6 89.8 96.8
Dont_know 10 3.0 3.2 92.8 100.0
NA 24 7.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C4D: Friendly talks with neighbors

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  # Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 37 11.1 11.5 11.1 11.5
Sometimes 126 37.7 39.0 48.8 50.5
Rarely_Never 156 46.7 48.3 95.5 98.8
Dont_know 4 1.2 1.2 96.7 100.0
NA 11 3.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 51 15.3 16.0 15.3 16.0
Sometimes 143 42.8 44.8 58.1 60.8
Rarely_Never 117 35.0 36.7 93.1 97.5
Dont_know 8 2.4 2.5 95.5 100.0
NA 15 4.5 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 154 46.1 48.7 46.1 48.7
Sometimes 93 27.8 29.4 74.0 78.2
Rarely_Never 52 15.6 16.5 89.5 94.6
Dont_know 17 5.1 5.4 94.6 100.0
NA 18 5.4 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

C4E: Ask neighbors for help

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  # Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 41 12.3 12.9 12.3 12.9
Sometimes 113 33.8 35.6 46.1 48.6
Rarely_Never 140 41.9 44.2 88.0 92.7
Dont_know 23 6.9 7.3 94.9 100.0
NA 17 5.1 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 44 13.2 14.1 13.2 14.1
Sometimes 136 40.7 43.5 53.9 57.5
Rarely_Never 114 34.1 36.4 88.0 93.9
Dont_know 19 5.7 6.1 93.7 100.0
NA 21 6.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 131 39.2 42.0 39.2 42.0
Sometimes 94 28.1 30.1 67.4 72.1
Rarely_Never 59 17.7 18.9 85.0 91.0
Dont_know 28 8.4 9.0 93.4 100.0
NA 22 6.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

D1: Treat you because of your race/ethnicity

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
  levels(d1aa) <- list(No="1",
                     Yes="2")
  d1aa <- ordered(d1aa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA" 
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1ab <- ordered(d1ab, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 160 47.9 49.1
Yes 166 49.7 50.9
NA 8 2.4 NA
Total 334 100.0 100.0 
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  # Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
  levels(d1ba) <- list(No="1",
                     Yes="2")
  d1ba <- ordered(d1ba, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  # Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1bb <- ordered(d1bb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 180 53.9 55.7
Yes 143 42.8 44.3
NA 11 3.3 NA
Total 334 100.0 100.0 
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  # Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
  levels(d1ca) <- list(No="1",
                     Yes="2")
  d1ca <- ordered(d1ca, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 83 24.9 25.4
Yes 244 73.1 74.6
NA 7 2.1 NA
Total 334 100.0 100.0 
  d1cb <- as.factor(d[,"d1cb"])
  # Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1cb <- ordered(d1cb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  # Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
  levels(d1da) <- list(No="1",
                     Yes="2")
  d1da <- ordered(d1da, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 232 69.5 72.3
Yes 89 26.6 27.7
NA 13 3.9 NA
Total 334 100.0 100.0 
  d1db <- as.factor(d[,"d1db"])
  # Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1db <- ordered(d1db, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  # Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
  levels(d1ea) <- list(No="1",
                     Yes="2")
  d1ea <- ordered(d1ea, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 242 72.5 74.2
Yes 84 25.1 25.8
NA 8 2.4 NA
Total 334 100.0 100.0 
  d1eb <- as.factor(d[,"d1eb"])
  # Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1eb <- ordered(d1eb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  # Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
  levels(d1fa) <- list(No="1",
                     Yes="2")
  d1fa <- ordered(d1fa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 239 71.6 74
Yes 84 25.1 26
NA 11 3.3 NA
Total 334 100.0 100 
  d1fb <- as.factor(d[,"d1fb"])
  # Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1fb <- ordered(d1fb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100 
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  # Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
  levels(d1ga) <- list(No="1",
                     Yes="2")
  d1ga <- ordered(d1ga, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 269 80.5 82.8
Yes 56 16.8 17.2
NA 9 2.7 NA
Total 334 100.0 100.0 
  d1gb <- as.factor(d[,"d1gb"])
  # Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1gb <- ordered(d1gb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 334 100 NA 100 NA
Total 334 100 100 100 100

D2: Medical Mistrust

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 49 14.7 15.1
Somewhat_Agree 137 41.0 42.3
Somewhat_Disagree 89 26.6 27.5
Strongly_Disagree 49 14.7 15.1
NA 10 3.0 NA
Total 334 100.0 100.0 
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 37 11.1 11.3
Somewhat_Agree 116 34.7 35.6
Somewhat_Disagree 105 31.4 32.2
Strongly_Disagree 68 20.4 20.9
NA 8 2.4 NA
Total 334 100.0 100.0 
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 77 23.1 24.1
Somewhat_Agree 100 29.9 31.3
Somewhat_Disagree 92 27.5 28.8
Strongly_Disagree 50 15.0 15.7
NA 15 4.5 NA
Total 334 100.0 100.0 
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 186 55.7 56.9
Somewhat_Agree 95 28.4 29.1
Somewhat_Disagree 30 9.0 9.2
Strongly_Disagree 16 4.8 4.9
NA 7 2.1 NA
Total 334 100.0 100.0 
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 13 3.9 4.1
Somewhat_Agree 52 15.6 16.3
Somewhat_Disagree 148 44.3 46.4
Strongly_Disagree 106 31.7 33.2
NA 15 4.5 NA
Total 334 100.0 100.0

D3A: Treated with less respect

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 95 28.4 28.7
Rarely 100 29.9 30.2
Sometimes 120 35.9 36.3
Often 16 4.8 4.8
NA 3 0.9 NA
Total 334 100.0 100.0 
#2
  d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 70 21.0 21.7
Rarely 94 28.1 29.1
Sometimes 140 41.9 43.3
Often 19 5.7 5.9
NA 11 3.3 NA
Total 334 100.0 100.0 
#3
  d3a3 <- as.factor(d[,"d3a3"])
  # Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 65 19.5 20.2
Rarely 74 22.2 23.0
Sometimes 141 42.2 43.8
Often 42 12.6 13.0
NA 12 3.6 NA
Total 334 100.0 100.0

D3B: Received poorer service

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 65 19.5 19.7
Rarely 127 38.0 38.5
Sometimes 121 36.2 36.7
Often 17 5.1 5.2
NA 4 1.2 NA
Total 334 100.0 100.0 
#2
  d3b2 <- as.factor(d[,"d3b2"])
  # Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 46 13.8 14.4
Rarely 118 35.3 36.9
Sometimes 139 41.6 43.4
Often 17 5.1 5.3
NA 14 4.2 NA
Total 334 100.0 100.0 
#3
  d3b3 <- as.factor(d[,"d3b3"])
  # Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 42 12.6 13.2
Rarely 98 29.3 30.9
Sometimes 145 43.4 45.7
Often 32 9.6 10.1
NA 17 5.1 NA
Total 334 100.0 100.0

D3C: Think you are not smart

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 77 23.1 23.6
Rarely 112 33.5 34.4
Sometimes 113 33.8 34.7
Often 24 7.2 7.4
NA 8 2.4 NA
Total 334 100.0 100.0 
#2
  d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 62 18.6 19.6
Rarely 102 30.5 32.2
Sometimes 131 39.2 41.3
Often 22 6.6 6.9
NA 17 5.1 NA
Total 334 100.0 100.0 
#3
  d3c3 <- as.factor(d[,"d3c3"])
  # Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 55 16.5 17.5
Rarely 83 24.9 26.4
Sometimes 140 41.9 44.6
Often 36 10.8 11.5
NA 20 6.0 NA
Total 334 100.0 100.0

D3D: Be afraid of you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 95 28.4 29.2
Rarely 97 29.0 29.8
Sometimes 108 32.3 33.2
Often 25 7.5 7.7
NA 9 2.7 NA
Total 334 100.0 100.0 
#2
  d3d2 <- as.factor(d[,"d3d2"])
  # Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 76 22.8 24.0
Rarely 81 24.3 25.6
Sometimes 131 39.2 41.3
Often 29 8.7 9.1
NA 17 5.1 NA
Total 334 100.0 100.0 
#3
  d3d3 <- as.factor(d[,"d3d3"])
  # Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 74 22.2 23.5
Rarely 84 25.1 26.7
Sometimes 119 35.6 37.8
Often 38 11.4 12.1
NA 19 5.7 NA
Total 334 100.0 100.0

D3E: Think you are dishonest

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 125 37.4 38.1
Rarely 112 33.5 34.1
Sometimes 74 22.2 22.6
Often 17 5.1 5.2
NA 6 1.8 NA
Total 334 100.0 100.0 
#2
  d3e2 <- as.factor(d[,"d3e2"])
  # Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 101 30.2 31.4
Rarely 115 34.4 35.7
Sometimes 89 26.6 27.6
Often 17 5.1 5.3
NA 12 3.6 NA
Total 334 100.0 100.0 
#3
  d3e3 <- as.factor(d[,"d3e3"])
  # Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 87 26.0 27.2
Rarely 96 28.7 30.0
Sometimes 112 33.5 35.0
Often 25 7.5 7.8
NA 14 4.2 NA
Total 334 100.0 100.0

D3F: Better than you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 46 13.8 14.1
Rarely 98 29.3 30.0
Sometimes 143 42.8 43.7
Often 40 12.0 12.2
NA 7 2.1 NA
Total 334 100.0 100.0 
#2
  d3f2 <- as.factor(d[,"d3f2"])
  # Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 39 11.7 12.1
Rarely 86 25.7 26.8
Sometimes 151 45.2 47.0
Often 45 13.5 14.0
NA 13 3.9 NA
Total 334 100.0 100.0 
#3
  d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 40 12.0 12.6
Rarely 72 21.6 22.6
Sometimes 148 44.3 46.5
Often 58 17.4 18.2
NA 16 4.8 NA
Total 334 100.0 100.0

D3G: Insulted

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 126 37.7 38.5
Rarely 132 39.5 40.4
Sometimes 62 18.6 19.0
Often 7 2.1 2.1
NA 7 2.1 NA
Total 334 100.0 100.0 
#2
  d3g2 <- as.factor(d[,"d3g2"])
  # Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 64 19.2 19.9
Rarely 148 44.3 46.1
Sometimes 100 29.9 31.2
Often 9 2.7 2.8
NA 13 3.9 NA
Total 334 100.0 100.0 
#3
  d3g3 <- as.factor(d[,"d3g3"])
  # Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 36 10.8 11.2
Rarely 113 33.8 35.3
Sometimes 134 40.1 41.9
Often 37 11.1 11.6
NA 14 4.2 NA
Total 334 100.0 100.0

D3H: Threatened or harassed

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 175 52.4 53.7
Rarely 109 32.6 33.4
Sometimes 38 11.4 11.7
Often 4 1.2 1.2
NA 8 2.4 NA
Total 334 100.0 100.0 
#2
  d3h2 <- as.factor(d[,"d3h2"])
  # Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 113 33.8 35.2
Rarely 143 42.8 44.5
Sometimes 55 16.5 17.1
Often 10 3.0 3.1
NA 13 3.9 NA
Total 334 100.0 100.0 
#3
  d3h3 <- as.factor(d[,"d3h3"])
  # Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 78 23.4 24.5
Rarely 118 35.3 37.0
Sometimes 104 31.1 32.6
Often 19 5.7 6.0
NA 15 4.5 NA
Total 334 100.0 100.0

D3I: Followed around in stores

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 120 35.9 36.6
Rarely 85 25.4 25.9
Sometimes 95 28.4 29.0
Often 28 8.4 8.5
NA 6 1.8 NA
Total 334 100.0 100.0 
#2
  d3i2 <- as.factor(d[,"d3i2"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 79 23.7 24.6
Rarely 96 28.7 29.9
Sometimes 113 33.8 35.2
Often 33 9.9 10.3
NA 13 3.9 NA
Total 334 100.0 100.0 
#3
  d3i3 <- as.factor(d[,"d3i3"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 59 17.7 18.4
Rarely 76 22.8 23.7
Sometimes 128 38.3 39.9
Often 58 17.4 18.1
NA 13 3.9 NA
Total 334 100.0 100.0

D3J: How stressful

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 101 30.2 30.9
Rarely 113 33.8 34.6
Sometimes 86 25.7 26.3
Often 27 8.1 8.3
NA 7 2.1 NA
Total 334 100.0 100.0 
#2
  d3j2 <- as.factor(d[,"d3j2"])
  # Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 71 21.3 22.3
Rarely 119 35.6 37.3
Sometimes 98 29.3 30.7
Often 31 9.3 9.7
NA 15 4.5 NA
Total 334 100.0 100.0 
#3
  d3j3 <- as.factor(d[,"d3j3"])
  # Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 64 19.2 20.1
Rarely 107 32.0 33.5
Sometimes 113 33.8 35.4
Often 35 10.5 11.0
NA 15 4.5 NA
Total 334 100.0 100.0

D4: How you currently see yourself

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 164 49.1 49.5 49.1 49.5
Somewhat_Agree 136 40.7 41.1 89.8 90.6
Somewhat_Disagree 25 7.5 7.6 97.3 98.2
Strongly_Disagree 6 1.8 1.8 99.1 100.0
NA 3 0.9 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  # Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 200 59.9 60.1 59.9 60.1
Somewhat_Agree 114 34.1 34.2 94.0 94.3
Somewhat_Disagree 18 5.4 5.4 99.4 99.7
Strongly_Disagree 1 0.3 0.3 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  # Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 148 44.3 44.6 44.3 44.6
Somewhat_Agree 145 43.4 43.7 87.7 88.3
Somewhat_Disagree 36 10.8 10.8 98.5 99.1
Strongly_Disagree 3 0.9 0.9 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  # Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 150 44.9 45.2 44.9 45.2
Somewhat_Agree 152 45.5 45.8 90.4 91.0
Somewhat_Disagree 28 8.4 8.4 98.8 99.4
Strongly_Disagree 2 0.6 0.6 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  # Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 143 42.8 42.9 42.8 42.9
Somewhat_Agree 135 40.4 40.5 83.2 83.5
Somewhat_Disagree 47 14.1 14.1 97.3 97.6
Strongly_Disagree 8 2.4 2.4 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  # Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 210 62.9 63.6 62.9 63.6
Somewhat_Agree 115 34.4 34.8 97.3 98.5
Somewhat_Disagree 4 1.2 1.2 98.5 99.7
Strongly_Disagree 1 0.3 0.3 98.8 100.0
NA 4 1.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  # Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 107 32.0 32.2 32.0 32.2
Somewhat_Agree 175 52.4 52.7 84.4 84.9
Somewhat_Disagree 40 12.0 12.0 96.4 97.0
Strongly_Disagree 10 3.0 3.0 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  # Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 198 59.3 59.5 59.3 59.5
Somewhat_Agree 116 34.7 34.8 94.0 94.3
Somewhat_Disagree 17 5.1 5.1 99.1 99.4
Strongly_Disagree 2 0.6 0.6 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
    # Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 178 53.3 53.6 53.3 53.6
Somewhat_Agree 133 39.8 40.1 93.1 93.7
Somewhat_Disagree 19 5.7 5.7 98.8 99.4
Strongly_Disagree 2 0.6 0.6 99.4 100.0
NA 2 0.6 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
    # Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 108 32.3 32.4 32.3 32.4
Somewhat_Agree 143 42.8 42.9 75.1 75.4
Somewhat_Disagree 70 21.0 21.0 96.1 96.4
Strongly_Disagree 12 3.6 3.6 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
    # Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 164 49.1 49.2 49.1 49.2
Somewhat_Agree 136 40.7 40.8 89.8 90.1
Somewhat_Disagree 29 8.7 8.7 98.5 98.8
Strongly_Disagree 4 1.2 1.2 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
    # Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 222 66.5 66.7 66.5 66.7
Somewhat_Agree 88 26.3 26.4 92.8 93.1
Somewhat_Disagree 20 6.0 6.0 98.8 99.1
Strongly_Disagree 3 0.9 0.9 99.7 100.0
NA 1 0.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

D5: Childhood

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  # Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 272 81.4 82.7
Yes 29 8.7 8.8
Dont_know_not_sure 26 7.8 7.9
Prefer_not_to_answer 2 0.6 0.6
NA 5 1.5 NA
Total 334 100.0 100.0 
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 224 67.1 67.7
Yes 95 28.4 28.7
Dont_know_not_sure 9 2.7 2.7
Prefer_not_to_answer 3 0.9 0.9
NA 3 0.9 NA
Total 334 100.0 100.0 
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 274 82.0 83.0
Yes 43 12.9 13.0
Dont_know_not_sure 10 3.0 3.0
Prefer_not_to_answer 3 0.9 0.9
NA 4 1.2 NA
Total 334 100.0 100.0 
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 279 83.5 84.5
Yes 43 12.9 13.0
Dont_know_not_sure 5 1.5 1.5
Prefer_not_to_answer 3 0.9 0.9
NA 4 1.2 NA
Total 334 100.0 100.0 
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 175 52.4 53.2
Yes 126 37.7 38.3
Not_married 13 3.9 4.0
Dont_know_not_sure 9 2.7 2.7
Prefer_not_to_answer 6 1.8 1.8
NA 5 1.5 NA
Total 334 100.0 100.0 
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 193 57.8 59.0
Once 26 7.8 8.0
More_than_once 60 18.0 18.3
Dont_know_not_sure 34 10.2 10.4
Prefer_not_to_answer 14 4.2 4.3
NA 7 2.1 NA
Total 334 100.0 100.0 
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 232 69.5 70.5
Once 13 3.9 4.0
More_than_once 59 17.7 17.9
Dont_know_not_sure 13 3.9 4.0
Prefer_not_to_answer 12 3.6 3.6
NA 5 1.5 NA
Total 334 100.0 100.0 
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 189 56.6 58.5
Once 16 4.8 5.0
More_than_once 91 27.2 28.2
Dont_know_not_sure 20 6.0 6.2
Prefer_not_to_answer 7 2.1 2.2
NA 11 3.3 NA
Total 334 100.0 100.0 
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  # Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 286 85.6 86.7
Once 18 5.4 5.5
More_than_once 12 3.6 3.6
Dont_know_not_sure 9 2.7 2.7
Prefer_not_to_answer 5 1.5 1.5
NA 4 1.2 NA
Total 334 100.0 100.0 
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  # Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 298 89.2 90.6
Once 15 4.5 4.6
More_than_once 7 2.1 2.1
Dont_know_not_sure 5 1.5 1.5
Prefer_not_to_answer 4 1.2 1.2
NA 5 1.5 NA
Total 334 100.0 100.0 
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  # Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 311 93.1 94.5
Once 8 2.4 2.4
More_than_once 3 0.9 0.9
Dont_know_not_sure 4 1.2 1.2
Prefer_not_to_answer 3 0.9 0.9
NA 5 1.5 NA
Total 334 100.0 100.0

E1: First indications

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other
# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1")

  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 263 78.7 100
NA 71 21.3 NA
Total 334 100.0 100 
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 79 23.7 100
NA 255 76.3 NA
Total 334 100.0 100 
#3
  e1_3 <- as.factor(d[,"e1_3"])
  e1_3[which(e1_3=="*")]<-"NA"
  levels(e1_3) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 76 22.8 100
NA 258 77.2 NA
Total 334 100.0 100 
#4
  e1_4 <- as.factor(d[,"e1_4"])
  e1_4[which(e1_4=="*")]<-"NA"
  levels(e1_4) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 4 1.2 100
NA 330 98.8 NA
Total 334 100.0 100 
#5
  e1_5 <- as.factor(d[,"e1_5"])
  e1_5[which(e1_5=="*")]<-"NA"
  levels(e1_5) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 5 1.5 100
NA 329 98.5 NA
Total 334 100.0 100

E1 Other: First indications

e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"

  new.d <- data.frame(new.d, e1other)
  new.d <- apply_labels(new.d, e1other = "e1other")
  temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
n % val%
Accident. Took father-in-law to doctor. Asked me if he examine me. I said yes. That’s it. 1 0.3 3.8
As a result of a physical exam 1 0.3 3.8
Back pain/stomach pain 1 0.3 3.8
Bled once during 1 0.3 3.8
Burning sensation 1 0.3 3.8
ED 1 0.3 3.8
Enlarged prostate 1 0.3 3.8
Enlarged prostrate. 1 0.3 3.8
Examination. 1 0.3 3.8
Eye pain. Doctor told me to go to my GP (general practitioner). GP said/tested for prostate. 1 0.3 3.8
Family history 1 0.3 3.8
Found out through kidney stone treatment 1 0.3 3.8
Frequent urination at night during sleeping hours. 1 0.3 3.8
Had blood in my urine. 1 0.3 3.8
I did not know. 1 0.3 3.8
I did test every 6 months missed 1 yr 6 months (my wife had cancer). 1 0.3 3.8
I wanted to buy life insurance the doctor tested me. 1 0.3 3.8
My Doc. sent me for an exam because of my age. 1 0.3 3.8
My father died of prostate cancer. 1 0.3 3.8
Nodules on groin 1 0.3 3.8
Physical. 1 0.3 3.8
Primary doctor concern about result of blood work 1 0.3 3.8
Semen in urine 1 0.3 3.8
To the DR for stomach pains 1 0.3 3.8
Unknown back pain 1 0.3 3.8
Weak urine stream, PSA of 13 on regular medical blood test 1 0.3 3.8
NA 308 92.2 NA
Total 334 100.0 100.0

E2: Before diagnosis

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more
# 1
  e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
  levels(e2aa) <- list(Yes="2",
                      No="1",
                      Dont_know="88")
  e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 47 14.1 14.5
No 258 77.2 79.6
Dont_know 19 5.7 5.9
NA 10 3.0 NA
Total 334 100.0 100.0 
#2
  e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
  levels(e2ab) <- list(One="1",
                      Two="2",
                      Three_more="3")
  e2ab <- ordered(e2ab, c("One","Two","Three_more"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 26 7.8 49.1
Two 13 3.9 24.5
Three_more 14 4.2 26.4
NA 281 84.1 NA
Total 334 100.0 100.0 
#3
  e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
  levels(e2ba) <- list(Yes="2",
                       No="1",
                       Dont_know="88")
  e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 181 54.2 58.2
No 70 21.0 22.5
Dont_know 60 18.0 19.3
NA 23 6.9 NA
Total 334 100.0 100.0 
#4
  e2bb <- as.factor(d[,"e2bb"])
  # Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
  levels(e2bb) <- list(One="1",
                      Two="2",
                      Three="3",
                      Four="4",
                      Five_more="5")
  e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 13 3.9 8.1
Two 20 6.0 12.4
Threem 0 0.0 0.0
Four 25 7.5 15.5
Five_more 103 30.8 64.0
NA 173 51.8 NA
Total 334 100.0 100.0

E3: Decision about PSA blood test

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made
  e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 71 21.3 21.6
With_family_or_friends 12 3.6 3.6
With_family_and_doctor 35 10.5 10.6
With_doctor 100 29.9 30.4
Doctor_made 96 28.7 29.2
Dont_know_or_remember 15 4.5 4.6
NA 5 1.5 NA
Total 334 100.0 100.0

E4: Understanding of aggressiveness

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember
  e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 127 38.0 38.5
Intermediate_risk 54 16.2 16.4
High_risk 78 23.4 23.6
Unknown_risk 34 10.2 10.3
Dont_know_or_remember 37 11.1 11.2
NA 4 1.2 NA
Total 334 100.0 100.0

E5: Gleason score

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know
  e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 59 17.7 18.0
Seven 60 18.0 18.3
Eight_to_ten 58 17.4 17.7
Dont_know 150 44.9 45.9
NA 7 2.1 NA
Total 334 100.0 100.0

E6: Understanding of stage

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage
  e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 246 73.7 75.0
Regional 18 5.4 5.5
Distant 7 2.1 2.1
Dont_know 57 17.1 17.4
NA 6 1.8 NA
Total 334 100.0 100.0

E7: MRI guided biopsy

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know
  e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e7 <- ordered(e7, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 156 46.7 47.6
Yes 112 33.5 34.1
Dont_know 60 18.0 18.3
NA 6 1.8 NA
Total 334 100.0 100.0

E8: Decision about treatment

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made
  e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 52 15.6 16.2
With_family_or_friends 45 13.5 14.0
With_family_and_doctor 121 36.2 37.7
With_doctor 77 23.1 24.0
Doctor_made 26 7.8 8.1
Dont_know_or_remember 0 0.0 0.0
NA 13 3.9 NA
Total 334 100.0 100.0

E9: The most important factors of tx

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer
  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 302 90.4 100
NA 32 9.6 NA
Total 334 100.0 100 
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 96 28.7 100
NA 238 71.3 NA
Total 334 100.0 100 
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 85 25.4 100
NA 249 74.6 NA
Total 334 100.0 100 
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 33 9.9 100
NA 301 90.1 NA
Total 334 100.0 100 
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 12 3.6 100
NA 322 96.4 NA
Total 334 100.0 100 
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 23 6.9 100
NA 311 93.1 NA
Total 334 100.0 100 
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 50 15 100
NA 284 85 NA
Total 334 100 100 
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 25 7.5 100
NA 309 92.5 NA
Total 334 100.0 100 
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 37 11.1 100
NA 297 88.9 NA
Total 334 100.0 100 
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 169 50.6 100
NA 165 49.4 NA
Total 334 100.0 100

E10: Recieved treatment

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 21 6.3 100
NA 313 93.7 NA
Total 334 100.0 100 
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 51 15.3 100
NA 283 84.7 NA
Total 334 100.0 100 
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 153 45.8 100
NA 181 54.2 NA
Total 334 100.0 100 
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 117 35 100
NA 217 65 NA
Total 334 100 100 
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 54 16.2 100
NA 280 83.8 NA
Total 334 100.0 100 
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 3 0.9 100
NA 331 99.1 NA
Total 334 100.0 100 
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 15 4.5 100
NA 319 95.5 NA
Total 334 100.0 100 
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 11 3.3 100
NA 323 96.7 NA
Total 334 100.0 100

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type
  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 169 50.6 100
NA 165 49.4 NA
Total 334 100.0 100 
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 7 2.1 100
NA 327 97.9 NA
Total 334 100.0 100 
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 11 3.3 100
NA 323 96.7 NA
Total 334 100.0 100

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type
  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 123 36.8 100
NA 211 63.2 NA
Total 334 100.0 100 
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 24 7.2 100
NA 310 92.8 NA
Total 334 100.0 100 
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 13 3.9 100
NA 321 96.1 NA
Total 334 100.0 100

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type
  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 73 21.9 100
NA 261 78.1 NA
Total 334 100.0 100 
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100 
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 11 3.3 100
NA 323 96.7 NA
Total 334 100.0 100 
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 4 1.2 100
NA 330 98.8 NA
Total 334 100.0 100 
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 11 3.3 100
NA 323 96.7 NA
Total 334 100.0 100

E11: Treatment decision

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much
  e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 9 2.7 2.7
A_little_bit 15 4.5 4.5
Somewhat 42 12.6 12.7
Quite_a_bit 100 29.9 30.3
Very_much 164 49.1 49.7
NA 4 1.2 NA
Total 334 100.0 100.0 
  e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 8 2.4 2.4
A_little_bit 14 4.2 4.2
Somewhat 42 12.6 12.7
Quite_a_bit 104 31.1 31.5
Very_much 162 48.5 49.1
NA 4 1.2 NA
Total 334 100.0 100.0 
  e11c <- as.factor(d[,"e11c"])
  # Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 31 9.3 9.4
A_little_bit 52 15.6 15.7
Somewhat 102 30.5 30.8
Quite_a_bit 75 22.5 22.7
Very_much 71 21.3 21.5
NA 3 0.9 NA
Total 334 100.0 100.0 
  e11d <- as.factor(d[,"e11d"])
  # Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 11 3.3 3.3
A_little_bit 10 3.0 3.0
Somewhat 48 14.4 14.5
Quite_a_bit 98 29.3 29.6
Very_much 164 49.1 49.5
NA 3 0.9 NA
Total 334 100.0 100.0 
  e11e <- as.factor(d[,"e11e"])
  # Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 15 4.5 4.5
A_little_bit 9 2.7 2.7
Somewhat 38 11.4 11.5
Quite_a_bit 66 19.8 19.9
Very_much 203 60.8 61.3
NA 3 0.9 NA
Total 334 100.0 100.0 
  e11f <- as.factor(d[,"e11f"])
  # Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 15 4.5 4.5
A_little_bit 20 6.0 6.0
Somewhat 56 16.8 16.9
Quite_a_bit 62 18.6 18.7
Very_much 178 53.3 53.8
NA 3 0.9 NA
Total 334 100.0 100.0

E12: Instructions from doctors or nurses

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e12 <- ordered(e12, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 23 6.9 7.0
Yes 292 87.4 88.8
Dont_know 14 4.2 4.3
NA 5 1.5 NA
Total 334 100.0 100.0

E13: # of PSA blood test

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure
  e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 7 2.1 2.1
One 5 1.5 1.5
Two 14 4.2 4.2
Three 32 9.6 9.6
Four_more 257 76.9 77.4
Dont_know 17 5.1 5.1
NA 2 0.6 NA
Total 334 100.0 100.0

E14: Be told PSA was rising

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e14 <- ordered(e14, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 226 67.7 68.3
Yes 90 26.9 27.2
Dont_know 15 4.5 4.5
NA 3 0.9 NA
Total 334 100.0 100.0

E15: Recurred or got worse

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e15 <- ordered(e15, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 282 84.4 85.7
Yes 41 12.3 12.5
Dont_know 6 1.8 1.8
NA 5 1.5 NA
Total 334 100.0 100.0

F1: Height

  • F1. How tall are you?
  f1cm <- d[,"f1cm"]
 
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
1 1 0.3 20
11 1 0.3 20
215 1 0.3 20
3 1 0.3 20
9 1 0.3 20
NA 329 98.5 NA
Total 334 100.0 100

F2: Weight

  • F2. How much do you current weight?
  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
*0 1 0.3 0.3
*99 1 0.3 0.3
1 * 1 0.3 0.3
115 1 0.3 0.3
118 1 0.3 0.3
120 1 0.3 0.3
125 2 0.6 0.6
130 1 0.3 0.3
137 1 0.3 0.3
138 1 0.3 0.3
140 2 0.6 0.6
145 2 0.6 0.6
148 1 0.3 0.3
150 4 1.2 1.3
152 1 0.3 0.3
155 2 0.6 0.6
158 1 0.3 0.3
160 9 2.7 2.9
162 1 0.3 0.3
163 1 0.3 0.3
165 4 1.2 1.3
166 1 0.3 0.3
167 2 0.6 0.6
168 3 0.9 1.0
169 1 0.3 0.3
170 7 2.1 2.3
172 2 0.6 0.6
173 3 0.9 1.0
175 5 1.5 1.6
176 2 0.6 0.6
177 3 0.9 1.0
178 3 0.9 1.0
180 7 2.1 2.3
181 1 0.3 0.3
182 1 0.3 0.3
183 3 0.9 1.0
184 1 0.3 0.3
185 7 2.1 2.3
186 3 0.9 1.0
187 2 0.6 0.6
188 3 0.9 1.0
189 1 0.3 0.3
190 8 2.4 2.6
191 2 0.6 0.6
192 3 0.9 1.0
193 1 0.3 0.3
195 5 1.5 1.6
196 1 0.3 0.3
197 1 0.3 0.3
198 2 0.6 0.6
199 1 0.3 0.3
2* 1 0.3 0.3
200 11 3.3 3.5
202 2 0.6 0.6
204 1 0.3 0.3
205 15 4.5 4.8
206 2 0.6 0.6
207 1 0.3 0.3
208 3 0.9 1.0
209 3 0.9 1.0
210 12 3.6 3.9
212 2 0.6 0.6
213 1 0.3 0.3
214 3 0.9 1.0
215 12 3.6 3.9
217 1 0.3 0.3
218 3 0.9 1.0
220 8 2.4 2.6
221 1 0.3 0.3
222 4 1.2 1.3
223 3 0.9 1.0
224 3 0.9 1.0
225 7 2.1 2.3
228 1 0.3 0.3
230 6 1.8 1.9
233 1 0.3 0.3
235 8 2.4 2.6
236 1 0.3 0.3
239 1 0.3 0.3
240 4 1.2 1.3
242 2 0.6 0.6
245 4 1.2 1.3
247 2 0.6 0.6
248 1 0.3 0.3
249 3 0.9 1.0
250 8 2.4 2.6
252 1 0.3 0.3
254 1 0.3 0.3
255 2 0.6 0.6
260 3 0.9 1.0
262 2 0.6 0.6
263 1 0.3 0.3
264 1 0.3 0.3
265 3 0.9 1.0
270 8 2.4 2.6
271 1 0.3 0.3
274 2 0.6 0.6
275 2 0.6 0.6
276 1 0.3 0.3
280 2 0.6 0.6
285 2 0.6 0.6
289 1 0.3 0.3
297 1 0.3 0.3
300 3 0.9 1.0
310 3 0.9 1.0
317 1 0.3 0.3
321 1 0.3 0.3
324 1 0.3 0.3
355 1 0.3 0.3
68 1 0.3 0.3
NA 24 7.2 NA
Total 334 100.0 100.0 
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
* 1 0.3 50
137 1 0.3 50
NA 332 99.4 NA
Total 334 100.0 100

F3: Exercise frequency

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise
  f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 73 21.9 22.4 21.9 22.4
Per_week_3_4 106 31.7 32.5 53.6 54.9
Per_week_1_2 90 26.9 27.6 80.5 82.5
Per_week_less_1 57 17.1 17.5 97.6 100.0
NA 8 2.4 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

F4: Minutes of exercise

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise
  f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 46 13.8 14.2 13.8 14.2
Between_30_min_1_hour 160 47.9 49.5 61.7 63.8
More_than_1_hour 82 24.6 25.4 86.2 89.2
Do_not_exercise 35 10.5 10.8 96.7 100.0
NA 11 3.3 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

F5: Drink alcohol frequency

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink
  f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1")
  f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Everyday 11 3.3 3.3 3.3 3.3
Per_week_5_6_times 18 5.4 5.4 8.7 8.8
Per_week_3_4_times 45 13.5 13.6 22.2 22.4
Per_week_1_2_times 43 12.9 13.0 35.0 35.3
Per_week_fewer_once 67 20.1 20.2 55.1 55.6
Not_drink 147 44.0 44.4 99.1 100.0
NA 3 0.9 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

F6: How many drinks

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink
  f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 23 6.9 7.0 6.9 7.0
One_to_two_drinks 154 46.1 47.1 53.0 54.1
Not_drink 150 44.9 45.9 97.9 100.0
NA 7 2.1 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

F7: Smoking history

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”
  f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
  levels(f7) <- list(Yes="2",
                     No="1")
  f7 <- ordered(f7, c("No","Yes"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 173 51.8 52.9 51.8 52.9
Yes 154 46.1 47.1 97.9 100.0
NA 7 2.1 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
10 2 0.6 1.6
12 2 0.6 1.6
13 6 1.8 4.7
14 3 0.9 2.4
15 10 3.0 7.9
16 12 3.6 9.4
17 15 4.5 11.8
18 13 3.9 10.2
19 11 3.3 8.7
20 14 4.2 11.0
21 10 3.0 7.9
22 7 2.1 5.5
23 3 0.9 2.4
25 3 0.9 2.4
26 2 0.6 1.6
27 1 0.3 0.8
28 3 0.9 2.4
30 1 0.3 0.8
32 1 0.3 0.8
33 1 0.3 0.8
34 1 0.3 0.8
35 1 0.3 0.8
40 2 0.6 1.6
45 1 0.3 0.8
48 1 0.3 0.8
50 1 0.3 0.8
NA 207 62.0 NA
Total 334 100.0 100.0 
  f7a <- as.factor(d[,"f7a"])
  # Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 58 17.4 37.9 17.4 37.9
Six_to_ten 48 14.4 31.4 31.7 69.3
Eleven_to_twenty 36 10.8 23.5 42.5 92.8
Twentyone_to_Thirty 7 2.1 4.6 44.6 97.4
Older_31 4 1.2 2.6 45.8 100.0
NA 181 54.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0 
  f7b <- as.factor(d[,"f7b"])
    # Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
  levels(f7b) <- list(No="1",
                     Yes="2")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 22 6.6 14.5
Yes 130 38.9 85.5
NA 182 54.5 NA
Total 334 100.0 100.0 
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
19 1 0.3 0.7
20 3 0.9 2.2
21 3 0.9 2.2
23 1 0.3 0.7
25 5 1.5 3.7
26 4 1.2 3.0
27 3 0.9 2.2
28 3 0.9 2.2
29 1 0.3 0.7
30 7 2.1 5.2
31 2 0.6 1.5
32 2 0.6 1.5
33 1 0.3 0.7
34 4 1.2 3.0
35 7 2.1 5.2
36 1 0.3 0.7
37 1 0.3 0.7
38 2 0.6 1.5
39 2 0.6 1.5
40 10 3.0 7.4
42 4 1.2 3.0
43 3 0.9 2.2
44 3 0.9 2.2
45 8 2.4 5.9
47 1 0.3 0.7
48 3 0.9 2.2
50 10 3.0 7.4
51 1 0.3 0.7
52 7 2.1 5.2
53 1 0.3 0.7
54 2 0.6 1.5
55 1 0.3 0.7
56 2 0.6 1.5
57 4 1.2 3.0
58 4 1.2 3.0
59 2 0.6 1.5
6 1 0.3 0.7
60 3 0.9 2.2
62 2 0.6 1.5
63 2 0.6 1.5
64 2 0.6 1.5
65 2 0.6 1.5
66 1 0.3 0.7
67 1 0.3 0.7
70 2 0.6 1.5
NA 199 59.6 NA
Total 334 100.0 100.0

G1: Marital status

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married
  g1 <- as.factor(d[,"g1"])
  # Make "*" to NA
g1[which(g1=="*")]<-"NA"
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 200 59.9 60.6 59.9 60.6
Separated 8 2.4 2.4 62.3 63.0
Divorced 64 19.2 19.4 81.4 82.4
Widowed 14 4.2 4.2 85.6 86.7
Never_Married 44 13.2 13.3 98.8 100.0
NA 4 1.2 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

G2: With whom do you live

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets
  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 84 25.1 100
NA 250 74.9 NA
Total 334 100.0 100 
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 203 60.8 100
NA 131 39.2 NA
Total 334 100.0 100 
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 60 18 100
NA 274 82 NA
Total 334 100 100 
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 8 2.4 100
NA 326 97.6 NA
Total 334 100.0 100 
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 25 7.5 100
NA 309 92.5 NA
Total 334 100.0 100

G3: Identify yourself

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer
  g3 <- as.factor(d[,"g3"])
  # Make "*" to NA
g3[which(g3=="*")]<-"NA"
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 315 94.3 95.2
Bisexual 2 0.6 0.6
homosexual 7 2.1 2.1
Other 1 0.3 0.3
Prefer_not_to_answer 6 1.8 1.8
NA 3 0.9 NA
Total 334 100.0 100.0

G3 Other: Identify yourself

g3other <- d[,"g3other"]
  new.d <- data.frame(new.d, g3other)
  new.d <- apply_labels(new.d, g3other = "g3other")
  temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
n % val%
No sex 1 0.3 50
Straight earl. 1 0.3 50
NA 332 99.4 NA
Total 334 100.0 100

G4: Education

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know
  g4a <- as.factor(d[,"g4a"])
  # Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 4 1.2 1.3
Some_high_school 18 5.4 5.9
High_school_graduate_GED 53 15.9 17.4
Vocational_school 9 2.7 3.0
Some_college 87 26.0 28.6
Associate_degree 36 10.8 11.8
College_graduate 43 12.9 14.1
Some_graduate_education 12 3.6 3.9
Graduate_degree 42 12.6 13.8
NA 30 9.0 NA
Total 334 100.0 100.0 
  g4b <- as.factor(d[,"g4b"])
    # Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 61 18.3 20.3
Some_high_school 37 11.1 12.3
High_school_graduate_GED 64 19.2 21.3
Vocational_school 11 3.3 3.7
Some_college 19 5.7 6.3
Associate_degree 9 2.7 3.0
College_graduate 18 5.4 6.0
Some_graduate_education 5 1.5 1.7
Graduate_degree 15 4.5 5.0
Dont_know 62 18.6 20.6
NA 33 9.9 NA
Total 334 100.0 100.0 
  g4c <- as.factor(d[,"g4c"])
    # Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 38 11.4 12.7
Some_high_school 32 9.6 10.7
High_school_graduate_GED 89 26.6 29.7
Vocational_school 17 5.1 5.7
Some_college 32 9.6 10.7
Associate_degree 20 6.0 6.7
College_graduate 23 6.9 7.7
Some_graduate_education 6 1.8 2.0
Graduate_degree 7 2.1 2.3
Dont_know 36 10.8 12.0
NA 34 10.2 NA
Total 334 100.0 100.0

G5: Job

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other
  g5 <- as.factor(d[,"g5"])
  # Make "*" to NA
g5[which(g5=="*")]<-"NA"
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 87 26.0 26.9
part_time 23 6.9 7.1
unemployed 9 2.7 2.8
Retired 163 48.8 50.5
disability_permanently 27 8.1 8.4
disability_for_a_time 3 0.9 0.9
Volunteer_work 0 0.0 0.0
Other 11 3.3 3.4
NA 11 3.3 NA
Total 334 100.0 100.0

G5 Other: job

g5other <- d[,"g5other"]
  new.d <- data.frame(new.d, g5other)
  new.d <- apply_labels(new.d, g5other = "g5other")
  temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
n % val%
Artist 1 0.3 5.6
Contract work 1 0.3 5.6
I also work as Uber driver part time 1 0.3 5.6
I work in the information/technology sector, due to the importation of foreign workers, I have on worked 4.5 years since 2005, racism has been a large reason for my unemployment 1 0.3 5.6
Manage income property 1 0.3 5.6
Musician 1 0.3 5.6
No work due to covid/district for which I sub, will hopefully return to normal in September. 1 0.3 5.6
On disability due to heart procedure. 1 0.3 5.6
On temporary disability (workers compensation) 1 0.3 5.6
Part time, business owner (pandemic) 1 0.3 5.6
Pastor 1 0.3 5.6
Retired 1 0.3 5.6
Retired I do in home care PT 1 0.3 5.6
Retired professional firefighter-Fire Captain 1 0.3 5.6
Self employed 3 0.9 16.7
Temp job 1 0.3 5.6
NA 316 94.6 NA
Total 334 100.0 100.0

G6: Health insurance

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance
  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 145 43.4 100
NA 189 56.6 NA
Total 334 100.0 100 
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 62 18.6 100
NA 272 81.4 NA
Total 334 100.0 100 
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 23 6.9 100
NA 311 93.1 NA
Total 334 100.0 100 
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 16 4.8 100
NA 318 95.2 NA
Total 334 100.0 100 
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
n % val%
Medicaid_state 30 9 100
NA 304 91 NA
Total 334 100 100 
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
n % val%
Medicare_government 130 38.9 100
NA 204 61.1 NA
Total 334 100.0 100 
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 38 11.4 100
NA 296 88.6 NA
Total 334 100.0 100 
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
n % val%
Do_not_have 1 0.3 100
NA 333 99.7 NA
Total 334 100.0 100

G7: Income

  • G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
    • 1=Less than $15,000
    • 2=$15,000 to $35,999
    • 3=$36,000 to $45,999
    • 4=$46,000 to $65,999
    • 5=$66,000 to $99,999
    • 6=$100,000 to $149,999
    • 7=$150,000 to $199,999
    • 8= $200,000 or more
  g7 <- as.factor(d[,"g7"])
  # Make "*" to NA
g7[which(g7=="*")]<-"NA"
  levels(g7) <- list(Less_than_15000="1",
                     Between_15000_35999="2",
                     Between_36000_45999="3",
                     Between_46000_65999="4",
                     Between_66000_99999="5",
                     Between_100000_149999= "6",
                     Between_150000_199999="7",
                     More_than_200000="8")
  g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))

  new.d <- data.frame(new.d, g7)
  new.d <- apply_labels(new.d, g7 = "income")
  temp.d <- data.frame (new.d, g7)  
  
  result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g7")
g7
n % val% %cum val%cum
Less_than_15000 23 6.9 7.5 6.9 7.5
Between_15000_35999 39 11.7 12.7 18.6 20.1
Between_36000_45999 35 10.5 11.4 29.0 31.5
Between_46000_65999 60 18.0 19.5 47.0 51.0
Between_66000_99999 62 18.6 20.1 65.6 71.1
Between_100000_149999 43 12.9 14.0 78.4 85.1
Between_150000_199999 23 6.9 7.5 85.3 92.5
More_than_200000 23 6.9 7.5 92.2 100.0
NA 26 7.8 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

G8: # people supported by income

  • G8. In the last calendar year, how many people, including yourself, were supported by your family income?
    • 1=1
    • 2=2
    • 3=3
    • 4=4
    • 5=5 or more
  g8 <- as.factor(d[,"g8"])
  # Make "*" to NA
g8[which(g8=="*")]<-"NA"
  levels(g8) <- list(One="1",
                     Two="2",
                     Three="3",
                     Four="4",
                     Five_or_more="5")
  g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))

  new.d <- data.frame(new.d, g8)
  new.d <- apply_labels(new.d, g8 = "people supported by income")
  temp.d <- data.frame (new.d, g8)  
  
  result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g8")
g8
n % val% %cum val%cum
One 118 35.3 36.2 35.3 36.2
Two 116 34.7 35.6 70.1 71.8
Three 46 13.8 14.1 83.8 85.9
Four 28 8.4 8.6 92.2 94.5
Five_or_more 18 5.4 5.5 97.6 100.0
NA 8 2.4 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

G9: Worry about finance

  • G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
      1. During young adult life (up to age 30):
      1. Age 31 (up to just before prostate cancer diagnosis):
      1. Current (from prostate cancer diagnosis to present):
      • 1=Not at all worried
      • 2=A little worried
      • 3=Somewhat worried
      • 4=Very worried
  g9a <- as.factor(d[,"g9a"])
  # Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
  levels(g9a) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9a)
  new.d <- apply_labels(new.d, g9a = "young adult life")
  temp.d <- data.frame (new.d, g9a)  
  result<-questionr::freq(temp.d$g9a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
n % val%
Not_worried 134 40.1 40.9
A_little_worried 112 33.5 34.1
Somewhat_worried 69 20.7 21.0
Very_worried 13 3.9 4.0
NA 6 1.8 NA
Total 334 100.0 100.0 
  g9b <- as.factor(d[,"g9b"])
    # Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
  levels(g9b) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9b)
  new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
  temp.d <- data.frame (new.d, g9b)  
  result<-questionr::freq(temp.d$g9b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
n % val%
Not_worried 166 49.7 50.9
A_little_worried 93 27.8 28.5
Somewhat_worried 54 16.2 16.6
Very_worried 13 3.9 4.0
NA 8 2.4 NA
Total 334 100.0 100.0 
  g9c <- as.factor(d[,"g9c"])
    # Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
  levels(g9c) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9c)
  new.d <- apply_labels(new.d, g9c = "current")
  temp.d <- data.frame (new.d, g9c)  
  result<-questionr::freq(temp.d$g9c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
n % val%
Not_worried 184 55.1 56.1
A_little_worried 72 21.6 22.0
Somewhat_worried 54 16.2 16.5
Very_worried 18 5.4 5.5
NA 6 1.8 NA
Total 334 100.0 100.0

G10:Own or rent a house

  • G10. Is the home you live in:
    • 1=Owned or being bought by you (or someone in the household)?
    • 2=Rented for money?
    • 3=Other
  g10 <- as.factor(d[,"g10"])
  # Make "*" to NA
g10[which(g10=="*")]<-"NA"
  levels(g10) <- list(Owned="1",
                     Rented="2",
                     Other="3")
  g10 <- ordered(g10, c("Owned","Rented","Other"))

  new.d <- data.frame(new.d, g10)
  new.d <- apply_labels(new.d, g10 = "Own or rent a house")
  temp.d <- data.frame (new.d, g10)  
  
  result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g10")
g10
n % val% %cum val%cum
Owned 217 65.0 66.8 65.0 66.8
Rented 99 29.6 30.5 94.6 97.2
Other 9 2.7 2.8 97.3 100.0
NA 9 2.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

G10 Other: Own or rent a house

g10other <- d[,"g10other"]
  new.d <- data.frame(new.d, g10other)
  new.d <- apply_labels(new.d, g10other = "g10other")
  temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
n % val%
Apartment. 1 0.3 8.3
I live with a friend who I help out. 1 0.3 8.3
I pay rent 1 0.3 8.3
I pay rent for a room 1 0.3 8.3
Inherited. 1 0.3 8.3
Living with uncle 1 0.3 8.3
Paid in full. 1 0.3 8.3
Pay association fees 1 0.3 8.3
Rent apt.. 1 0.3 8.3
Renting 1 0.3 8.3
Senior citizen housing 1 0.3 8.3
Staying with my daughter and family 1 0.3 8.3
NA 322 96.4 NA
Total 334 100.0 100.0

G11:Lose current sources

  • G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
    • 1=Less than 1 month
    • 2=1 to 2 months
    • 3=3 to 6 months
    • 4=More than 6 months
  g11 <- as.factor(d[,"g11"])
  # Make "*" to NA
g11[which(g11=="*")]<-"NA"
  levels(g11) <- list(Less_than_1_month="1",
                     One_to_two_month="2",
                     Three_to_six_month="3",
                     More_than_6_months="4")
  g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))

  new.d <- data.frame(new.d, g11)
  new.d <- apply_labels(new.d, g11 = "ose current sources")
  temp.d <- data.frame (new.d, g11)  
  
  result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g11")
g11
n % val% %cum val%cum
Less_than_1_month 32 9.6 10.2 9.6 10.2
One_to_two_month 47 14.1 14.9 23.7 25.1
Three_to_six_month 88 26.3 27.9 50.0 53.0
More_than_6_months 148 44.3 47.0 94.3 100.0
NA 19 5.7 NA 100.0 NA
Total 334 100.0 100.0 100.0 100.0

G12: Today’s date

  • G12. Please enter today’s date.
  g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
  new.d <- data.frame(new.d, g12)
  new.d <- apply_labels(new.d, g12 = "today’s date")
  #temp.d <- data.frame (new.d.1, g12) 
  
  summarytools::view(dfSummary(new.d$g12, style = 'grid',
                               max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 g12 [labelled, Date] today’s date
min : 2019-01-07
med : 2020-06-05
max : 2020-12-28
range : 1y 11m 21d
 199 distinct values 3 (0.9%)

Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09